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: "efficiently estimating motif statistics of large networks"
  Abstract: Exploring statistics of locally connected subgraph patterns (also known as network motifs) has helped researchers better understand the structure and function of biological and online social networks (OSNs). Nowadays the massive size of some critical networks -- often stored in already overloaded relational databases -- effectively limits the rate at which nodes and edges can be explored, making it a challenge to accurately discover subgraph statistics. In this work, we propose sampling methods to accurately estimate subgraph statistics from as few queried nodes as possible. We present sampling algorithms that efficiently and accurately estimate subgraph properties of massive networks. Our algorithms require no pre-computation or complete network topology information. At the same time, we provide theoretical guarantees of convergence. We perform experiments using widely known data sets, and show that for the same accuracy, our algorithms require an order of magnitude less queries (samples) than the current state-of-the-art algorithms.

Connected through hyperedge HE-1 (induced by source paper "improved mixing time for k subgraph sampling"):
  with members:
    - "waddling random walk fast and accurate mining of motif statistics in large graphs"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "the slashdot zoo mining a social network with negative edges"

Connected through hyperedge HE-2 (induced by source paper "a survey on subgraph counting concepts algorithms and applications to network motifs and graphlets"):
  with members:
    - "heterogeneous network motifs"
    - "variational principle for scale free network motifs"
    - "the sketching complexity of graph and hypergraph counting"
    - "motifs in temporal networks"
    - "large scale network motif learning with compression"
    - "butterfly counting in bipartite networks"
    - "estimation of graphlet statistics"
    - "combinatorial algorithm for counting small induced graphs and orbits"

Connected through hyperedge HE-3 (induced by source paper "motivo fast motif counting via succinct color coding and adaptive sampling"):
  with members:
    - "subgraph counting color coding beyond trees"
    - "waddling random walk fast and accurate mining of motif statistics in large graphs"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "a general framework for estimating graphlet statistics via random walk"
    - "a fast and provable method for estimating clique counts using tur an s theorem"
    - "a fast sampling method of exploring graphlet degrees of large directed and undirected graphs"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "practical graph isomorphism ii"

Connected through hyperedge HE-4 (induced by source paper "one step estimation of networked population size respondent driven capture recapture with anonymity"):
  with members:
    - "graph size estimation"
    - "walking on a graph with a magnifying glass stratified sampling via weighted random walks"

Connected through hyperedge HE-5 (induced by source paper "mining frequent patterns in evolving graphs"):
  with members:
    - "waddling random walk fast and accurate mining of motif statistics in large graphs"
    - "a general framework for estimating graphlet statistics via random walk"

Connected through hyperedge HE-6 (induced by source paper "what would a graph look like in this layout a machine learning approach to large graph visualization"):
  with members:
    - "a general framework for estimating graphlet statistics via random walk"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"

Connected through hyperedge HE-7 (induced by source paper "sampling online social networks by random walk with indirect jumps"):
  with members:
    - "inferring networks of substitutable and complementary products"
    - "leveraging history for faster sampling of online social networks"
    - "romantic partnerships and the dispersion of social ties a network analysis of relationship status on facebook"
    - "triadic measures on graphs the power of wedge sampling"
    - "faster random walks by rewiring online social networks on the fly"
    - "multigraph sampling of online social networks"
    - "estimating and sampling graphs with multidimensional random walks"

Connected through hyperedge HE-8 (induced by source paper "large scale network motif analysis using compression"):
  with members:
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "practical graph isomorphism ii"
    - "substructure discovery using minimum description length and background knowledge"

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)