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 "bidirectional pagerank estimation from average case to worst case"):
  Abstract: We present a new algorithm for estimating the Personalized PageRank (PPR) between a source and target node on undirected graphs, with sublinear running-time guarantees over the worst-case choice of source and target nodes. Our work builds on a recent line of work on bidirectional estimators for PPR, which obtained sublinear running-time guarantees but in an average-case sense, for a uniformly random choice of target node. Crucially, we show how the reversibility of random walks on undirected networks can be exploited to convert average-case to worst-case guarantees. While past bidirectional methods combine forward random walks with reverse local pushes, our algorithm combines forward local pushes with reverse random walks. We also discuss how to modify our methods to estimate random-walk probabilities for any length distribution, thereby obtaining fast algorithms for estimating general graph diffusions, including the heat kernel, on undirected networks.

Includes members:
  Member "personalized pagerank estimation and search a bidirectional approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "empirical policy evaluation with supergraphs")
  Member "a note on the pagerank of undirected graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "graph similarity using pagerank and persistent homology")
  Member "fast bidirectional probability estimation in markov models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "empirical policy evaluation with supergraphs")
  Member "heat kernel based community detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "connectivity driven communication in multi agent reinforcement learning through diffusion processes on graphs")
  Member "fast ppr scaling personalized pagerank estimation for large graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient algorithms for approximate single source personalized pagerank queries")
  Member "personalized pagerank with node dependent restart":
    also connected through other hyperedges:
      (none)
  Member "pagerank beyond the web":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "nonlinearity networks a 2020 vision")
  Member "sublinear algorithms for local graph centrality estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "walking randomly massively and efficiently")

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.DS(Data Structures and Algorithms)