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: "the power of an example hidden set size approximation using group queries and conditional sampling"
  Abstract: We study a basic problem of approximating the size of an unknown set $S$ in a known universe $U$. We consider two versions of the problem. In both versions the algorithm can specify subsets $T\subseteq U$. In the first version, which we refer to as the group query or subset query version, the algorithm is told whether $T\cap S$ is non-empty. In the second version, which we refer to as the subset sampling version, if $T\cap S$ is non-empty, then the algorithm receives a uniformly selected element from $T\cap S$. We study the difference between these two versions under different conditions on the subsets that the algorithm may query/sample, and in both the case that the algorithm is adaptive and the case where it is non-adaptive. In particular we focus on a natural family of allowed subsets, which correspond to intervals, as well as variants of this family.

Connected through hyperedge HE-1 (induced by source paper "optimal randomized group testing algorithm to determine the number of defectives"):
  with members:
    - "adaptive group testing algorithms to estimate the number of defectives"

Connected through hyperedge HE-2 (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"
    - "approximately counting triangles in sublinear time"
    - "a near optimal sublinear time algorithm for approximating the minimum vertex cover size"

Connected through hyperedge HE-3 (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"
    - "approximately counting triangles in sublinear time"

Connected through hyperedge HE-4 (induced by source paper "hyperedge estimation using polylogarithmic subset queries"):
  with members:
    - "parameterized query complexity of hitting set using stability of sunflowers"
    - "fine grained reductions from approximate counting to decision"
    - "edge estimation with independent set oracles"
    - "on approximating the number of k cliques in sublinear time"
    - "computing exact minimum cuts without knowing the graph"
    - "approximately counting triangles in sublinear time"
    - "finding cycles and trees in sublinear time"

Connected through hyperedge HE-5 (induced by source paper "provable and practical approximations for the degree distribution using sublinear graph samples"):
  with members:
    - "measuring and modeling bipartite graphs with community structure"
    - "sublinear time estimation of degree distribution moments the degeneracy connection"
    - "maxoutprobe an algorithm for increasing the size of partially observed networks"
    - "approximately counting triangles in sublinear time"
    - "catching the head tail and everything in between a streaming algorithm for the degree distribution"
    - "graph sample and hold a framework for big graph analytics"
    - "network sampling from static to streaming graphs"
    - "community structure and scale free collections of erdős renyi graphs"

Connected through hyperedge HE-6 (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"
    - "a simpler sublinear algorithm for approximating the triangle count"
    - "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-7 (induced by source paper "estimation of sparsity via simple measurements"):
  with members:
    - "nonadaptive group testing with random set of defectives"
    - "compressed sensing without sparsity assumptions"
    - "linear boolean classification coding and the critical problem"
    - "necessary and sufficient conditions for sparsity pattern recovery"

Connected through hyperedge HE-8 (induced by source paper "a chasm between identity and equivalence testing with conditional queries"):
  with members:
    - "testing probability distributions underlying aggregated data"
    - "optimal algorithms for testing closeness of discrete distributions"
    - "testing probability distributions using conditional samples"
    - "on the power of conditional samples in distribution testing"
    - "testing closeness of discrete distributions"
    - "streaming and sublinear approximation of entropy and information distances"

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)