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 "nonlocal pagerank"):
  Abstract: In this work we introduce and study a nonlocal version of the PageRank. In our approach, the random walker explores the graph using longer excursions than just moving between neighboring nodes. As a result, the corresponding ranking of the nodes, which takes into account a long-range interaction between them, does not exhibit concentration phenomena typical for spectral rankings taking into account just local interactions. We show that the predictive value of the rankings obtained using our proposals is considerably improved on different real world problems.

Includes members:
  Member "a framework for second order eigenvector centralities and clustering coefficients":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "clustering in graphs and hypergraphs with categorical edge labels")
  Member "three hypergraph eigenvector centralities":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hypergraph cuts with general splitting functions")
  Member "a nonlinear spectral method for core periphery detection in networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "total variation based community detection using a nonlinear optimization approach")
  Member "a modularity based spectral method for simultaneous community and anti community detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spectral clustering of signed graphs via matrix power means")
  Member "the spacey random walk a stochastic process for higher order data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hetespaceywalk a heterogeneous spacey random walk for heterogeneous information network embedding")
  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 "localization and centrality in networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "factorized graph representations for semi supervised learning from sparse data")
  Member "total communicability as a centrality measure":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a general centrality framework based on node navigability")

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)