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: "estimating clique composition and size distributions from sampled network data"
  Abstract: Cliques are defined as complete graphs or subgraphs; they are the strongest form of cohesive subgroup, and are of interest in both social science and engineering contexts. In this paper we show how to efficiently estimate the distribution of clique sizes from a probability sample of nodes obtained from a graph (e.g., by independence or link-trace sampling). We introduce two types of unbiased estimators, one of which exploits labeling of sampled nodes neighbors and one of which does not require this information. We compare the estimators on a variety of real-world graphs and provide suggestions for their use. We generalize our estimators to cases in which cliques are distinguished not only by size but also by node attributes, allowing us to estimate clique composition by size. Finally, we apply our methodology to a sample of Facebook users to estimate the clique size distribution by gender over the social graph.

Connected through hyperedge HE-1 (induced by source paper "estimating subgraph frequencies with or without attributes from egocentrically sampled data"):
  with members:
    - "social structure of facebook networks"
    - "listing all maximal cliques in large sparse real world graphs"

Connected through hyperedge HE-2 (induced by source paper "coloring large complex networks"):
  with members:
    - "hop doubling label indexing for point to point distance querying on scale free networks"
    - "graph sample and hold a framework for big graph analytics"
    - "graph cluster randomization network exposure to multiple universes"
    - "dimension reduction via colour refinement"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"
    - "network sampling from static to streaming graphs"
    - "exact algorithms for maximum clique a computational study"
    - "exact algorithms for maximum clique a computational study"

Connected through hyperedge HE-3 (induced by source paper "efficiently estimating motif statistics of large networks"):
  with members:
    - "practical graph isomorphism ii"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"
    - "estimating and sampling graphs with multidimensional random walks"
    - "the slashdot zoo mining a social network with negative edges"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

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)