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 simpler sublinear algorithm for approximating the triangle count"
  Abstract: A recent result of Eden, Levi, and Ron (ECCC 2015) provides a sublinear time algorithm to estimate the number of triangles in a graph. Given an undirected graph G, one can query the degree of a vertex, the existence of an edge between vertices, and the ith neighbor of a vertex. Suppose the graph has n vertices, m edges, and t triangles. In this model, Eden et al provided a O(poly(e 1 logn)(n/t 1/3 +m 3/2 /t)) time algorithm to get a (1+e)-multiplicative approximation for t, the triangle count. This paper provides a simpler algorithm with the same running time (up to differences in the poly(e 1 logn) factor) that has a substantially simpler analysis.

Connected through hyperedge HE-1 (induced by source paper "slim graph practical lossy graph compression for approximate graph processing storage and analytics"):
  with members:
    - "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"
    - "graph processing on fpgas taxonomy survey challenges"
    - "fatpaths routing in supercomputers data centers and clouds with low diameter networks when shortest paths fall short"
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "active access a mechanism for high performance distributed data centric computations"
    - "communication efficient jaccard similarity for high performance distributed genome comparisons"
    - "local computation algorithms for spanners"
    - "survey and taxonomy of lossless graph compression and space efficient graph representations"

Connected through hyperedge HE-2 (induced by source paper "testing graph clusterability algorithms and lower bounds"):
  with members:
    - "testing bounded arboricity"
    - "on approximating the number of k cliques in sublinear time"
    - "estimating graph parameters from random order streams"
    - "on sampling edges almost uniformly"
    - "sublinear time estimation of degree distribution moments the degeneracy connection"
    - "testing cluster structure of graphs"
    - "approximately counting triangles in sublinear time"
    - "partitioning into expanders"

Connected through hyperedge HE-3 (induced by source paper "edge estimation with independent set oracles"):
  with members:
    - "fine grained reductions from approximate counting to decision"
    - "on approximating the number of k cliques in sublinear time"
    - "on sampling edges almost uniformly"
    - "approximately counting triangles in sublinear time"
    - "the power of an example hidden set size approximation using group queries and conditional sampling"
    - "shortest paths in intersection graphs of unit disks"
    - "a near optimal sublinear time algorithm for approximating the minimum vertex cover size"

Connected through hyperedge HE-4 (induced by source paper "sublinear time algorithms for counting star subgraphs with applications to join selectivity estimation"):
  with members:
    - "approximately counting triangles in sublinear time"
    - "testing probability distributions underlying aggregated data"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"
    - "a near optimal sublinear time algorithm for approximating the minimum vertex cover size"

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.DS(Data Structures and Algorithms)