human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "a new generalization of edge overlap to weighted networks"
  Abstract: Finding the strength of an edge in a network has always been a big demand. In the context of social#N#networks, it allows to estimate the relationship strength between users. The best-known method to compute#N#edge strength is the Neighbourhood Overlap. It computes the ratio of common neighbours to all neighbours#N#of an edge terminal nodes. This method has been initially proposed for unweighted networks and later#N#extended for weighted ones. These two versions of the method are not mathematically equivalent: In fact,#N#an unweighted network is commonly considered as weighted with all edge weights equal to one. Using both#N#existent versions of Neighbourhood Overlap on such network produce completely different values. In this#N#paper, we tackle this problem and propose a new generalization for Neighbourhood Overlap that works#N#equally for unweighted and weighted networks. Experiment performed on networks with various#N#parameters showed similar performance of our measure to the existing measures.

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.SI(Social and Information Networks)