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 "localization in seeded pagerank"):
  Abstract: Seeded PageRank is an important network analysis tool for identifying and studying regions nearby a given set of nodes, which are called seeds. The seeded PageRank vector is the stationary distribution of a random walk that randomly resets at the seed nodes. Intuitively, this vector is concentrated nearby the given seeds, but is mathematically non-zero for all nodes in a connected graph. We study this concentration, or localization, and show a sublinear upper bound on the number of entries required to approximate seeded PageRank on all graphs with a natural type of skewed-degree sequence---similar to those that arise in many real-world networks. Experiments with both real-world and synthetic graphs give further evidence to the idea that the degree sequence of a graph has a major influence on the localization behavior of seeded PageRank. Moreover, we establish that this localization is non-trivial by showing that complete-bipartite graphs produce seeded PageRank vectors that cannot be approximated with a sublinear number of non-zeros.

Includes members:
  Member "bidirectional pagerank estimation from average case to worst case":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep node ranking structural network embedding and end to end node classification")
  Member "fast ppr scaling personalized pagerank estimation for large graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient algorithms for approximate single source personalized pagerank queries")
  Member "a scalable generative graph model with community structure":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "recent advances in scalable network generation")
  Member "pagerank beyond the web":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "nonlinearity networks a 2020 vision")
  Member "sublinear algorithms for local graph centrality estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "walking randomly massively and efficiently")
  Member "fast matrix computations for pairwise and columnwise commute times and katz scores":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fast computation of katz index for efficient processing of link prediction queries")
  Member "quick detection of nodes with large degrees":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "star sampling with and without replacement")
  Member "a sequential algorithm for generating random graphs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "random features strengthen graph neural 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)