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: "graph size estimation"
  Abstract: Many online networks are not fully known and are often studied via sampling. Random Walk (RW) based techniques are the current state-of-the-art for estimating nodal attributes and local graph properties, but estimating global properties remains a challenge. In this paper, we are interested in a fundamental property of this type - the graph size N, i.e., the number of its nodes. Existing methods for estimating N are (i) inefficient and (ii) cannot be easily used with RW sampling due to dependence between successive samples. In this paper, we address both problems. First, we propose IE (Induced Edges), an efficient technique for estimating N from an independence sample of graph's nodes. IE exploits the edges induced on the sampled nodes. Second, we introduce SafetyMargin, a method that corrects estimators for dependence in RW samples. Finally, we combine these two stand-alone techniques to obtain a RW-based graph size estimator. We evaluate our approach in simulations on a wide range of real-life topologies, and on several samples of Facebook. IE with SafetyMargin typically requires at least 10 times fewer samples than the state-of-the-art techniques (over 100 times in the case of Facebook) for the same estimation error.

Connected through hyperedge HE-1 (induced by source paper "true nonlinear dynamics from incomplete networks"):
  with members:
    - "towards unbiased bfs sampling"
    - "estimating and sampling graphs with multidimensional random walks"

Connected through hyperedge HE-2 (induced by source paper "estimating shell index in a graph with local information"):
  with members:
    - "global rank estimation"
    - "a faster method to estimate closeness centrality ranking"
    - "degree ranking using local information"
    - "modeling memetics using edge diversity"
    - "pseudo cores the terminus of an intelligent viral meme s trajectory"
    - "rank me thou shalln t compare me"
    - "evolving models for meso scale structures"
    - "understanding spreading patterns on social networks based on network topology"

Connected through hyperedge HE-3 (induced by source paper "one step estimation of networked population size respondent driven capture recapture with anonymity"):
  with members:
    - "efficiently estimating motif statistics of large networks"
    - "walking on a graph with a magnifying glass stratified sampling via weighted random walks"

Connected through hyperedge HE-4 (induced by source paper "degree ranking using local information"):
  with members:
    - "a faster method to estimate closeness centrality ranking"
    - "ant inspired density estimation via random walks"
    - "sublinear time estimation of degree distribution moments the arboricity connection"
    - "rank me thou shalln t compare me"
    - "understanding spreading patterns on social networks based on network topology"
    - "estimating the degree centrality ranking of a node"
    - "walk not wait faster sampling over online social networks"
    - "real time enhanced random sampling of online social networks"

Connected through hyperedge HE-5 (induced by source paper "ant inspired density estimation via random walks"):
  with members:
    - "limits for rumor spreading in stochastic populations"
    - "on coalescence time in graphs when is coalescing as fast as meeting"
    - "a walk in facebook uniform sampling of users in online social networks"

Connected through hyperedge HE-6 (induced by source paper "rank me thou shalln t compare me"):
  with members:
    - "modeling memetics using edge diversity"
    - "evolving models for meso scale structures"
    - "understanding spreading patterns on social networks based on network topology"
    - "estimating the degree centrality ranking of a node"

Connected through hyperedge HE-7 (induced by source paper "estimating the degree centrality ranking of a node"):
  with members:
    - "incremental algorithms for network management and analysis based on closeness centrality"
    - "betweenness centrality incremental and faster"
    - "how to make the top ten approximating pagerank from in degree"
    - "fast approximation of centrality"

Connected through hyperedge HE-8 (induced by source paper "a survey and taxonomy of graph sampling"):
  with members:
    - "identifying communities and key vertices by reconstructing networks from samples"
    - "coarse grained topology estimation via graph sampling"
    - "triangles to capture social cohesion"
    - "towards unbiased bfs sampling"
    - "multigraph sampling of online social networks"
    - "estimating and sampling graphs with multidimensional random walks"
    - "twice ramanujan sparsifiers"
    - "a tutorial on spectral clustering"

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.SI(Social and Information Networks)