Hyperbolic geometry has gained attention for its ability to naturally embed hierarchical and tree-like structures with low distortion, outperforming Euclidean spaces in various graph representation tasks. While previous work has demonstrated the advantage of graph neural networks embedding in hyperbolic space for link prediction and node classification, the benefits for graph classification remain less understood. Moreover, these studies typically attribute the benefits of hyperbolic models to the hierarchical or tree-like nature of graph structures, often neglecting the important role that node features play in leveraging these geometric advantages. With this in mind, we designed an experiment specifically aimed at evaluating the interplay between geometry and node features in graph classification, creating a dataset composed exclusively of tree-structured graphs. Each graph is generated by sampling the number of children per node at each level from a predefined range of branching factors, which varies across levels. The dataset defines two distinct classes based on these branching factor patterns. Node features are either random, structural embeddings obtained via node2vec, or layout-based embeddings derived from kamada-kawai algorithm, which provide a strong hierarchical prior. We evaluated a fully hyperbolic graph neural network against its Euclidean counterpart and a standard Graph Convolutional Network (GCN), using node embeddings learned in low-dimensional latent spaces. Across all feature types, the Euclidean counterpart consistently outperformed the hyperbolic one. This indicates that the benefits of hyperbolic geometry do not arise solely from its alignment with global graph structure. These findings call for a critical reassessment of hyperbolic models in graph classification tasks where preserving graph distances is not essential.