Networks abstracted as graph lose some information related to the super-dyadic relation among the nodes. We find natural occurrence of hyperedges in co-authorship, co-citation, social networks, e-mail networks, weblog networks etc. Treating these networks as hypergraph preserves the super-dyadic relations. But the primal graph or Gaifmann graph associated with hypergraphs converts each hyperedge to a clique losing again the n-ary relationship among nodes. We aim to measure Shapley Value based centrality on these networks without losing the super-dyadic information. For this purpose, we use co-operative games on single graph representation of a hypergraph such that Shapley value can be computed efficiently. We propose several methods to generate simpler graphs from hypergraphs and study the efficacy of the centrality scores computed on these constructions.