In hypergraphs identifying the influential nodes is a crucial task and they spread information more rapidly to the maximum nodes of the entire network. To reduce this, centrality measures are widely utilized to find top central nodes in hypergraph. Various traditional centrality measures are available in hypergraphs such as degree, closeness, betweenness, and harmonic centralities. These traditional centrality measures are mainly focused on the immediate neighbors and shortest paths. In this research, we proposed a novel local centrality (LC) and it is integrated with degree, Local Relative Average Shortest Path (LRASP), and similarity measures, which focus on the local nodes of the hypergraph. By using our proposed Local centrality (LC) measure we identified top-ranked nodes and compared them with top-ranked values of other centralities. To assess the performance of our proposed measure, we utilized two datasets and evaluation metrics: Kendall Tau and the SIR model. Kendall Tau is used to assess the similarity between the traditional and proposed measures, while the SIR model evaluates how our proposed method simulates the spread of information across the network and compared with traditional metrics.