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 "strong localization in personalized pagerank vectors"):
  Abstract: The personalized PageRank diffusion is a fundamental tool in network analysis tasks like community detection and link prediction. It models the spread of a quantity from a set of seed nodes, and it has been observed to stay localized near this seed set. We derive an upperbound on the number of entries necessary to approximate a personalized PageRank vector in graphs with skewed degree sequences. This bound shows localization under mild assumptions on the maximum and minimum degrees. Experimental results on random graphs with these degree sequences show the bound is loose and support a conjectured bound.

Includes members:
  Member "heat kernel based community detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "connectivity driven communication in multi agent reinforcement learning through diffusion processes on graphs")
  Member "fast matrix computations for pairwise and columnwise commute times and katz scores":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (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-4 (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)