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: "learning social circles in ego networks based on multi view social graphs"
  Abstract: Automatic social circle detection in ego-networks is becoming a fundamentally important task for social network analysis, which can be used for privacy protection or interest group recommendation. So far, most studies focused on how to detect overlapping circles or how to perform detection using both network structure and its node profiles. This paper asks an orthogonal research question: how to detect social circles by leveraging the multiple views of the network structure? As a first step, we crawl ego networks from Twitter to construct multi-view social graphs with six views, including two relationship views, three interaction views and one content view. Then, we apply both standard and our modified multi-view spectral clustering methods on the graph to detect circles. Based on extensive automatic and manual evaluations, we deliver two major findings: first, multi-view clustering methods detect better circles than single-view clustering methods; second, by presuming the sparse social graphs are incomplete, our modified method detects better circles than the standard method that ignores such potential incompleteness. In particular, we believe a direct application of the standard method on a possibly incomplete graph may yield a biased result. By integrating theories from spectral clustering and matrix perturbation, we prove an information-theoretic upper bound for such bias and discuss how it may be affected by the characteristics of the social network.

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)