<p>Finding key nodes in higher-order networks is a crucial task, as they disseminating the information across the large portions of the networks of evaluating these top influential nodes we utilize the centrality measures within the network. In this study, we are working on hypergraphs, which are an advanced form of graphs capable of capturing the higher-order interactions. Hypergraphs allow edges to connect multiple nodes simultaneously. Various centrality metrics are available to evaluate the influential nodes in hypergraphs. We proposed Local RASP (Local Relative Average Shortest Path), a new measure that is designed to emphasize the network’s local structure. Local RASP evaluates the relative change in the hypernetwork once a node is deleted. Local RASP centrality delivers the best results compared with degree centrality, betweenness centrality, closeness centrality, harmonic centrality and with one recent centrality gravity. We assessed the performance of our proposed centrality measure ability to identify the key nodes by simulating the information propagation with SIR model and applied Kendall Tau correlation to compare the proposed method with other basic and recent centrality metrics.</p>