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 "approximately counting triangles in sublinear time"):
  Abstract: We consider the problem of estimating the number of triangles in a graph. This problem has been extensively studied in both theory and practice, but all existing algorithms read the entire graph. In this work we design a sub linear-time algorithm for approximating the number of triangles in a graph, where the algorithm is given query access to the graph. The allowed queries are degree queries, vertex-pair queries and neighbor queries. We show that for any given approximation parameter 0 <epsilon<1, the algorithm provides an estimate hat{t} such that with high constant probability, (1-epsilon) t<hat{t}<(1+epsilon)t, where t is the number of triangles in the graph G. The expected query complexity of the algorithm is O(n/t{1/3} + min {m, m {3/2}/t}) poly(log n, 1/epsilon), where n is the number of vertices in the graph and m is the number of edges, and the expected running time is (n/t{1/3} + m {3/2}/t) poly(log n, 1/epsilon). We also prove that a#x03A9;(n/t {1/3} + min {m, m {3/2}/t}) queries are necessary, thus establishing that the query complexity of this algorithm is optimal up to polylogarithmic factors in n (and the dependence on 1/epsilon).

Includes members:
  Member "counting triangles in massive graphs with mapreduce":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "parallel algorithms for small subgraph counting")
  Member "graph sample and hold a framework for big graph analytics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep graph similarity learning a survey")
  Member "wedge sampling for computing clustering coefficients and triangle counts on large graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the power of pivoting for exact clique counting")
  Member "parallel triangle counting in massive streaming graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fleet butterfly estimation from a bipartite graph stream")
  Member "a space efficient streaming algorithm for triangle counting using the birthday paradox":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "community structure and scale free collections of erdős renyi graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "artificial benchmark for community detection abcd fast random graph model with community structure")
  Member "efficient triangle counting in large graphs via degree based vertex partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "parallel algorithms for small subgraph counting")
  Member "colorful triangle counting and a mapreduce implementation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "parallel algorithms for small subgraph counting")

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.DS(Data Structures and Algorithms)