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: "on sampling edges almost uniformly"
  Abstract: We consider the problem of sampling an edge almost uniformly from an unknown graph, $G = (V, E)$. Access to the graph is provided via queries of the following types: (1) uniform vertex queries, (2) degree queries, and (3) neighbor queries. We describe an algorithm that returns a random edge $e \in E$ using $\tilde{O}(n / \sqrt{\varepsilon m})$ queries in expectation, where $n = |V|$ is the number of vertices, and $m = |E|$ is the number of edges, such that each edge $e$ is sampled with probability $(1 \pm \varepsilon)/m$. We prove that our algorithm is optimal in the sense that any algorithm that samples an edge from an almost-uniform distribution must perform $\Omega(n / \sqrt{m})$ queries.

Connected through hyperedge HE-1 (induced by source paper "computing and testing small connectivity in near linear time and queries via fast local cut algorithms"):
  with members:
    - "a faster local algorithm for detecting bounded size cuts with applications to higher connectivity problems"
    - "computing and testing small vertex connectivity in near linear time and queries"
    - "breaking quadratic time for small vertex connectivity and an approximation scheme"
    - "a new algorithm for decremental single source shortest paths with applications to vertex capacitated flow and cut problems"
    - "expander decomposition and pruning faster stronger and simpler"
    - "faster algorithms for computing maximal 2 connected subgraphs in sparse directed graphs"
    - "local flow partitioning for faster edge connectivity"
    - "on fully dynamic graph sparsifiers"

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"
    - "sublinear time algorithms for counting star subgraphs with applications to join selectivity estimation"
    - "approximately counting triangles in sublinear time"
    - "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-3 (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"
    - "sublinear time estimation of degree distribution moments the degeneracy connection"
    - "approximately counting triangles in sublinear time"
    - "listing all maximal cliques in large sparse real world graphs"

Connected through hyperedge HE-4 (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"
    - "computing exact minimum cuts without knowing the graph"
    - "approximately counting triangles in sublinear time"
    - "the power of an example hidden set size approximation using group queries and conditional sampling"

Connected through hyperedge HE-5 (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"
    - "sublinear time estimation of degree distribution moments the degeneracy connection"
    - "testing cluster structure of graphs"
    - "approximately counting triangles in sublinear time"
    - "a simpler sublinear algorithm for approximating the triangle count"
    - "partitioning into expanders"

Connected through hyperedge HE-6 (induced by source paper "on approximating the number of k cliques in sublinear time"):
  with members:
    - "lower bounds for approximating graph parameters via communication complexity"
    - "sublinear time estimation of degree distribution moments the degeneracy connection"
    - "clique counting in mapreduce algorithms and experiments"
    - "approximately counting triangles in sublinear time"
    - "if the current clique algorithms are optimal so is valiant s parser"
    - "parallel triangle counting in massive streaming graphs"
    - "community structure and scale free collections of erdős renyi graphs"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"

Connected through hyperedge HE-7 (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"
    - "approximately counting triangles in sublinear time"
    - "a simpler sublinear algorithm for approximating the triangle count"
    - "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-8 (induced by source paper "testing bounded arboricity"):
  with members:
    - "planar graphs random walks and bipartiteness testing"
    - "a quasi polynomial time partition oracle for graphs with an excluded minor"
    - "an efficient partitioning oracle for bounded treewidth graphs"
    - "finding cycles and trees in sublinear time"
    - "linear time algorithms for finding a dominating set of fixed size in degenerated graphs"

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.CC(Computational Complexity)