Understanding processes such as information dissemination and network resilience relies on pinpointing influential nodes within complex relationships. Conventional centrality measures, which are based on shortest path calculations, fall short in hypergraphs due to the complexity of paths involving multiple simultaneous relationships. Traditional metrics oversimplify the node influence and the stability of hyperedges. The Isolating Centrality measure is introduced in hypergraphs to address the drawback it specifically focuses on local structures. ISC assesses how the removal of a node affects the connectivity of hyperedges, providing a more detailed approach for identifying influential nodes. We assess the superiority performance of ISC over conventional centrality metrics and are evaluated through correlation analysis and the SIR model.