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 "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.

Includes members:
  Member "social structure of facebook networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compressive sensing for cut improvement and local clustering")
  Member "coarse grained topology estimation via graph sampling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey and taxonomy of graph sampling")
  Member "on the bias of bfs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sampling promotes community structure in social and information networks")
  Member "community structure in large networks natural cluster sizes and the absence of large well defined clusters":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "certified robustness of community detection against adversarial structural perturbation via randomized smoothing")
  Member "what is the real size of a sampled network the case of the internet":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "inferring structural characteristics of networks with strong and weak ties from fixed choice surveys")

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)