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: "a fast and provable method for estimating clique counts using tur an s theorem"
  Abstract: Clique counts reveal important properties about the structure of massive graphs, especially social networks. The simple setting of just 3-cliques (triangles) has received much attention from the research community. For larger cliques (even, say 6-cliques) the problem quickly becomes intractable because of combinatorial explosion. Most methods used for triangle counting do not scale for large cliques, and existing algorithms require massive parallelism to be feasible. #R##N#We present a new randomized algorithm that provably approximates the number of k-cliques, for any constant k. The key insight is the use of (strengthenings of) the classic Turan's theorem: this claims that if the edge density of a graph is sufficiently high, the k-clique density must be non-trivial. We define a combinatorial structure called a Turan shadow, the construction of which leads to fast algorithms for clique counting. #R##N#We design a practical heuristic, called TURAN-SHADOW, based on this theoretical algorithm, and test it on a large class of test graphs. In all cases,TURAN-SHADOW has less than 2% error, in a fraction of the time used by well-tuned exact algorithms. We do detailed comparisons with a range of other sampling algorithms, and find that TURAN-SHADOW is generally much faster and more accurate. For example, TURAN-SHADOW estimates all cliques numbers up to size 10 in social network with over a hundred million edges. This is done in less than three hours on a single commodity machine.

Connected through hyperedge HE-1 (induced by source paper "hypergraph motifs concepts algorithms and discoveries"):
  with members:
    - "how much and when do we need higher order information in hypergraphs a case study on hyperedge prediction"
    - "heterogeneous network motifs"
    - "edmot an edge enhancement approach for motif aware community detection"
    - "hypergraph clustering with categorical edge labels"
    - "motivo fast motif counting via succinct color coding and adaptive sampling"
    - "mining frequent patterns in evolving graphs"
    - "rept a streaming algorithm of approximating global and local triangle counts in parallel"
    - "simplicial closure and higher order link prediction"

Connected through hyperedge HE-2 (induced by source paper "efficiently counting vertex orbits of all 5 vertex subgraphs by evoke"):
  with members:
    - "heterogeneous network motifs"
    - "higher order clustering in networks"
    - "detecting strong ties using network motifs"
    - "wrs waiting room sampling for accurate triangle counting in real graph streams"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "higher order organization of complex networks"
    - "scalable motif aware graph clustering"
    - "beyond triangles a distributed framework for estimating 3 profiles of large graphs"

Connected through hyperedge HE-3 (induced by source paper "retrieving top weighted triangles in graphs"):
  with members:
    - "graph based semi supervised active learning for edge flows"
    - "simplicial closure and higher order link prediction"
    - "tiered sampling an efficient method for approximate counting sparse motifs in massive graph streams"
    - "edge based wedge sampling to estimate triangle counts in very large graphs"
    - "science askscience and badscience on the coexistence of highly related communities"
    - "higher order organization of complex networks"
    - "approximately counting triangles in sublinear time"
    - "role discovery in networks"

Connected through hyperedge HE-4 (induced by source paper "parallel algorithms for butterfly computations"):
  with members:
    - "theoretically efficient parallel graph algorithms can be fast and scalable"
    - "vertex priority based butterfly counting for large scale bipartite networks"
    - "butterfly counting in bipartite networks"
    - "peeling bipartite networks for dense subgraph discovery"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "measuring and modeling bipartite graphs with community structure"
    - "graphlet decomposition framework algorithms and applications"
    - "distributed estimation of graph 4 profiles"

Connected through hyperedge HE-5 (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 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"
    - "efficiently estimating motif statistics of large networks"
    - "practical graph isomorphism ii"

Connected through hyperedge HE-6 (induced by source paper "pangolin an efficient and flexible graph mining system on cpu and gpu"):
  with members:
    - "single machine graph analytics on massive datasets using intel optane dc persistent memory"
    - "higher order organization of complex networks"
    - "the gap benchmark suite"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"

Connected through hyperedge HE-7 (induced by source paper "a sampling framework for counting temporal motifs"):
  with members:
    - "a chronological edge driven approach to temporal subgraph isomorphism"
    - "motifs in temporal networks"
    - "enumerating maximal cliques in link streams with durations"
    - "when is a network a network multi order graphical model selection in pathways and temporal networks"
    - "science askscience and badscience on the coexistence of highly related communities"
    - "higher order organization of complex networks"
    - "scalable motif aware graph clustering"
    - "approximately counting triangles in sublinear time"

Connected through hyperedge HE-8 (induced by source paper "butterfly counting in bipartite networks"):
  with members:
    - "edge based wedge sampling to estimate triangle counts in very large graphs"
    - "peeling bipartite networks for dense subgraph discovery"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "measuring and modeling bipartite graphs with community structure"
    - "graphlet decomposition framework algorithms and applications"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "wedge sampling for computing clustering coefficients and triangle counts on large graphs"
    - "triadic measures on graphs the power of wedge sampling"

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)