human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a framework for second order eigenvector centralities and clustering coefficients"):
  Abstract: We propose and analyse a general tensor-based framework for incorporating second order features into network measures. This approach allows us to combine traditional pairwise links with information that records whether triples of nodes are involved in wedges or triangles. Our treatment covers classical spectral methods and recently proposed cases from the literature, but we also identify many interesting extensions. In particular, we define a mutually-reinforcing (spectral) version of the classical clustering coefficient. The underlying object of study is a constrained nonlinear eigenvalue problem associated with a cubic tensor. Using recent results from nonlinear Perron--Frobenius theory, we establish existence and uniqueness under appropriate conditions, and show that the new spectral measures can be computed efficiently with a nonlinear power method. To illustrate the added value of the new formulation, we analyse the measures on a class of synthetic networks. We also give computational results on centrality and link prediction for real-world networks.

Includes members:
  Member "higher order clustering in networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "parameterized objectives and algorithms for clustering bipartite graphs and hypergraphs")
  Member "three hypergraph eigenvector centralities":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hypergraph cuts with general splitting functions")
  Member "motif and hypergraph correlation clustering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "parameterized objectives and algorithms for clustering bipartite graphs and hypergraphs")
  Member "simplicial closure and higher order link prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "node and layer eigenvector centralities for multiplex networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "supracentrality analysis of temporal networks with directed interlayer coupling")
  Member "higher order organization of complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "pagerank beyond the web":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "nonlinearity networks a 2020 vision")
  Member "predicting triadic closure in networks using communicability distance functions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fast link prediction for large networks using spectral embedding")

Question: Please tell me which class the source paper 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)