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: "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).

Connected through hyperedge HE-1 (induced by source paper "parallel algorithms for small subgraph counting"):
  with members:
    - "log diameter rounds algorithms for 2 vertex and 2 edge connectivity"
    - "dynamic algorithms for the massively parallel computation model"
    - "linear time subgraph counting graph degeneracy and the chasm at size six"
    - "exponentially faster massively parallel maximal matching"
    - "faster algorithms for edge connectivity via random 2 out contractions"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "approximating edit distance in truly subquadratic time quantum and mapreduce"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"

Connected through hyperedge HE-2 (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-3 (induced by source paper "linear time subgraph counting graph degeneracy and the chasm at size six"):
  with members:
    - "a simple sublinear time algorithm for counting arbitrary subgraphs via edge sampling"
    - "homomorphisms are a good basis for counting small subgraphs"
    - "on approximating the number of k cliques in sublinear time"
    - "combinatorial algorithm for counting small induced graphs and orbits"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "beyond triangles a distributed framework for estimating 3 profiles of large graphs"
    - "distributed estimation of graph 4 profiles"
    - "counting triangles in massive graphs with mapreduce"

Connected through hyperedge HE-4 (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"
    - "a fast and provable method for estimating clique counts using tur an s theorem"
    - "role discovery in networks"

Connected through hyperedge HE-5 (induced by source paper "querying a matrix through matrix vector products"):
  with members:
    - "tight query complexity lower bounds for pca via finite sample deformed wigner law"
    - "improved algorithms for adaptive compressed sensing"
    - "testing matrix rank optimally"
    - "near optimal linear decision trees for k sum and related problems"
    - "on estimating maximum matching size in graph streams"
    - "embeddings of schatten norms with applications to data streams"
    - "on approximating functions of the singular values in a stream"
    - "matrix norms in data streams faster multi pass and row order"

Connected through hyperedge HE-6 (induced by source paper "nearly optimal edge estimation with independent set queries"):
  with members:
    - "a simple sublinear time algorithm for counting arbitrary subgraphs via edge sampling"
    - "fiding forbidden minors in sublinear time a o n 1 2 o 1 query one sided tester for minor closed properties on bounded degree graphs"
    - "edge estimation with independent set oracles"
    - "on approximating the number of k cliques in sublinear time"
    - "on sampling edges almost uniformly"
    - "sublinear time algorithms for counting star subgraphs with applications to join selectivity estimation"
    - "the power of an example hidden set size approximation using group queries and conditional sampling"
    - "a near optimal sublinear time algorithm for approximating the minimum vertex cover size"

Connected through hyperedge HE-7 (induced by source paper "the arboricity captures the complexity of sampling edges"):
  with members:
    - "a simple sublinear time algorithm for counting arbitrary subgraphs via edge sampling"
    - "faster sublinear approximations of k cliques for low arboricity graphs"
    - "lower bounds for approximating graph parameters via communication complexity"
    - "testing bounded arboricity"
    - "on approximating the number of k cliques in sublinear time"
    - "on sampling edges almost uniformly"
    - "sublinear time estimation of degree distribution moments the degeneracy connection"
    - "listing all maximal cliques in large sparse real world graphs"

Connected through hyperedge HE-8 (induced by source paper "inner product oracle can estimate and sample"):
  with members:
    - "sublinear time numerical linear algebra for structured matrices"
    - "fine grained reductions from approximate counting to decision"
    - "testing matrix rank optimally"
    - "edge estimation with independent set oracles"
    - "on sampling edges almost uniformly"
    - "computing exact minimum cuts without knowing the graph"
    - "the power of an example hidden set size approximation using group queries and conditional 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.DS(Data Structures and Algorithms)