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: "three hypergraph eigenvector centralities"
  Abstract: Eigenvector centrality is a standard network analysis tool for determining the importance of (or ranking of) entities in a connected system that is represented by a graph. However, many complex systems and datasets have natural multi-way interactions that are more faithfully modeled by a hypergraph. Here we extend the notion of graph eigenvector centrality to uniform hypergraphs. Traditional graph eigenvector centralities are given by a positive eigenvector of the adjacency matrix, which is guaranteed to exist by the Perron-Frobenius theorem under some mild conditions. The natural representation of a hypergraph is a hypermatrix (colloquially, a tensor). Using recently established Perron-Frobenius theory for tensors, we develop three tensor eigenvectors centralities for hypergraphs, each with different interpretations. We show that these centralities can reveal different information on real-world data by analyzing hypergraphs constructed from n-gram frequencies, co-tagging on stack exchange, and drug combinations observed in patient emergency room visits.

Connected through hyperedge HE-1 (induced by source paper "hypergraph cuts with general splitting functions"):
  with members:
    - "nonlinearity networks a 2020 vision"
    - "simplicial closure and higher order link prediction"
    - "network flow based refinement for multilevel hypergraph partitioning"
    - "hypergraph partitioning for sparse matrix matrix multiplication"
    - "higher order organization of complex networks"
    - "a simple and strongly local flow based method for cut improvement"
    - "simple and fast rounding algorithms for directed and node weighted multiway cut"
    - "local distribution and the symmetry gap approximability of multiway partitioning problems"

Connected through hyperedge HE-2 (induced by source paper "nonlocal pagerank"):
  with members:
    - "a framework for second order eigenvector centralities and clustering coefficients"
    - "a nonlinear spectral method for core periphery detection in networks"
    - "a modularity based spectral method for simultaneous community and anti community detection"
    - "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-3 (induced by source paper "clustering in graphs and hypergraphs with categorical edge labels"):
  with members:
    - "nonlinearity networks a 2020 vision"
    - "a framework for second order eigenvector centralities and clustering coefficients"
    - "solving linear programs in the current matrix multiplication time"
    - "motif and hypergraph correlation clustering"
    - "simplicial closure and higher order link prediction"
    - "correlation clustering generalized"
    - "spectral sparsification of simplicial complexes for clustering and label propagation"
    - "inhomogoenous hypergraph clustering with applications"

Connected through hyperedge HE-4 (induced by source paper "network shrinkage estimation"):
  with members:
    - "higher order ranking and link prediction from closing triangles to closing higher order motifs"
    - "on sampling from massive graph streams"
    - "representing higher order dependencies in networks"
    - "graphlet decomposition framework algorithms and applications"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "graph sample and hold a framework for big graph analytics"
    - "triadic measures on graphs the power of wedge sampling"
    - "near optimal entrywise sampling for data matrices"

Connected through hyperedge HE-5 (induced by source paper "a framework for second order eigenvector centralities and clustering coefficients"):
  with members:
    - "higher order clustering in networks"
    - "motif and hypergraph correlation clustering"
    - "simplicial closure and higher order link prediction"
    - "node and layer eigenvector centralities for multiplex networks"
    - "higher order organization of complex networks"
    - "pagerank beyond the web"
    - "predicting triadic closure in networks using communicability distance functions"

Connected through hyperedge HE-6 (induced by source paper "planted hitting set recovery in hypergraphs"):
  with members:
    - "core fringe link prediction"
    - "simplicial closure and higher order link prediction"
    - "found graph data and planted vertex covers"
    - "bubing massive crawling for the masses"
    - "social networks under stress"
    - "pagerank beyond the web"
    - "axioms for centrality"

Connected through hyperedge HE-7 (induced by source paper "multi dimensional multilayer nonlinear and dynamic hits"):
  with members:
    - "node and layer eigenvector centralities for multiplex 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)