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: "centrality metrics and localization in core periphery networks"
  Abstract: Two concepts of centrality have been defined in complex networks. The first considers the centrality of a node and many different metrics for it have been defined (e.g. eigenvector centrality, PageRank, non-backtracking centrality, etc). The second is related to large scale organization of the network, the core-periphery structure, composed by a dense core plus an outlying and loosely-connected periphery. In this paper we investigate the relation between these two concepts. We consider networks generated via the stochastic block model, or its degree corrected version, with a core-periphery structure and we investigate the centrality properties of the core nodes and the ability of several centrality metrics to identify them. We find that the three measures with the best performance are marginals obtained with belief propagation, PageRank, and degree centrality, while non-backtracking and eigenvector centrality (or MINRES [10], showed to be equivalent to the latter in the large network limit) perform worse in the investigated networks.

Connected through hyperedge HE-1 (induced by source paper "spectral density of equitable core periphery graphs"):
  with members:
    - "spectral partitioning in equitable graphs"
    - "identification of core periphery structure in networks"
    - "localization and centrality in networks"
    - "core periphery structure in networks"

Connected through hyperedge HE-2 (induced by source paper "using core periphery structure to predict high centrality nodes in time varying networks"):
  with members:
    - "time series analysis of temporal networks"
    - "understanding stability of noisy networks through centrality measures and local connections"
    - "predicting node degree centrality with the node prominence profile"
    - "locally estimating core numbers"
    - "centrality metric for dynamic networks"

Connected through hyperedge HE-3 (induced by source paper "detection of core periphery structure in networks using spectral methods and geodesic paths"):
  with members:
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "identification of core periphery structure in networks"
    - "role discovery in networks"
    - "a local perspective on community structure in multilayer networks"
    - "core periphery structure in networks"
    - "social structure of facebook networks"
    - "eigenvector synchronization graph rigidity and the molecule problem"
    - "structure and overlaps of communities in networks"

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)