human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "approximating pagerank locally with sublinear query complexity"
  Abstract: Can we approximate the centrality score of a given node by exploring only a vanishing fraction of the graph? In this paper we develop a combination of techniques that we apply to the case of PageRank, where the centrality of a node depends on \emph{every} arc in the graph. We obtain an algorithm that, given any one node in any $n$-node graph, with probability $1-\delta$ returns a multiplicative $(1\pm\epsilon)$ approximation of its PageRank score using at most $O\big(n^{\frac{2}{3}}\sqrt[3]{\log(n/\delta)}\,\ln(1/\delta)^\frac{1}{3}\epsilon^{-\frac{2}{3}}\big) = \tilde{O}(n^{\frac{2}{3}})$ graph exploration queries -- where a query returns either a given node's list of neighbours, or a node chosen uniformly at random. Until now, every algorithm required in general $\Omega(n)$ queries. We also show that this upper bound is essentially optimal by proving an almost matching lower bound. Our techniques can be applied to other centrality measures, even though we leave the analysis of the resulting bounds for future work.

Connected through hyperedge HE-1 (induced by source paper "bidirectional pagerank estimation from average case to worst case"):
  with members:
    - "personalized pagerank estimation and search a bidirectional approach"
    - "a note on the pagerank of undirected graphs"
    - "fast bidirectional probability estimation in markov models"
    - "heat kernel based community detection"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "personalized pagerank with node dependent restart"
    - "pagerank beyond the web"
    - "sublinear algorithms for local graph centrality estimation"

Connected through hyperedge HE-2 (induced by source paper "how to select the largest k elements from evolving data"):
  with members:
    - "sorting and selection with imprecise comparisons"
    - "algorithms for multi armed bandit problems"
    - "sublinear algorithms for local graph centrality estimation"

Question: Please tell me which class the center node 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)