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 modularity based spectral method for simultaneous community and anti community detection"
  Abstract: In a graph or complex network, communities and anti-communities are node sets whose modularity attains extremely large values, positive and negative, respectively. We consider the simultaneous detection of communities and anti-communities, by looking at spectral methods based on various matrix-based definitions of the modularity of a vertex set. Invariant subspaces associated to extreme eigenvalues of these matrices provide indications on the presence of both kinds of modular structure in the network. The localization of the relevant invariant subspaces can be estimated by looking at particular matrix angles based on Frobenius inner products.

Connected through hyperedge HE-1 (induced by source paper "nonlocal pagerank"):
  with members:
    - "a framework for second order eigenvector centralities and clustering coefficients"
    - "three hypergraph eigenvector centralities"
    - "a nonlinear spectral method for core periphery detection in networks"
    - "the spacey random walk a stochastic process for higher order data"
    - "higher order organization of complex networks"
    - "localization and centrality in networks"
    - "total communicability as a centrality measure"

Connected through hyperedge HE-2 (induced by source paper "spectral clustering of signed graphs via matrix power means"):
  with members:
    - "balance in signed networks"
    - "an mbo scheme for clustering and semi supervised clustering of signed networks"
    - "node classification for signed networks using diffuse interface methods"
    - "signed graph convolutional network"
    - "on spectral partitioning of signed graphs"
    - "sne signed network embedding"
    - "spectral theory of unsigned and signed graphs applications to graph clustering a survey"
    - "on the troll trust model for edge sign prediction in social networks"

Connected through hyperedge HE-3 (induced by source paper "data clustering and graph partitioning via simulated mixing"):
  with members:
    - "compressive spectral clustering"
    - "accelerated spectral clustering using graph filtering of random signals"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "a tutorial on spectral clustering"

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)