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 local perspective on community structure in multilayer networks"):
  Abstract: The analysis of multilayer networks is among the most active areas of network science, and there are now several methods to detect dense "communities" of nodes in multilayer networks. One way to define a community is as a set of nodes that trap a diffusion-like dynamical process (usually a random walk) for a long time. In this view, communities are sets of nodes that create bottlenecks to the spreading of a dynamical process on a network. We analyze the local behavior of different random walks on multiplex networks (which are multilayer networks in which different layers correspond to different types of edges) and show that they have very different bottlenecks that hence correspond to rather different notions of what it means for a set of nodes to be a good community. This has direct implications for the behavior of community-detection methods that are based on these random walks.

Includes members:
  Member "block models and personalized pagerank":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "diffusion improves graph learning")
  Member "think locally act locally detection of small medium sized and large communities in large networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "migration networks applications of network analysis to large scale human mobility")
  Member "spreading processes in multilayer networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep learning approach on information diffusion in heterogeneous networks")
  Member "community detection in multiplex networks using locally adaptive random walks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep node ranking structural network embedding and end to end node classification")
  Member "seeded pagerank solution paths":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a short graph fourier transform via personalized pagerank vectors")
  Member "overlapping community detection using neighborhood inflated seed expansion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "community detection in node attributed social networks a survey")
  Member "pagerank beyond the web":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "nonlinearity networks a 2020 vision")
  Member "ranking and clustering of nodes in networks with smart teleportation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multiscale dynamical embeddings of complex networks")

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)