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 "the arboricity captures the complexity of sampling edges"):
  Abstract: In this paper, we revisit the problem of sampling edges in an unknown graph $G = (V, E)$ from a distribution that is (pointwise) almost uniform over $E$. We consider the case where there is some a priori upper bound on the arboriciy of $G$. Given query access to a graph $G$ over $n$ vertices and of average degree $d$ and arboricity at most $\alpha$, we design an algorithm that performs $O\!\left(\frac{\alpha}{d} \cdot \frac{\log^3 n}{\varepsilon}\right)$ queries in expectation and returns an edge in the graph such that every edge $e \in E$ is sampled with probability $(1 \pm \varepsilon)/m$. The algorithm performs two types of queries: degree queries and neighbor queries. We show that the upper bound is tight (up to poly-logarithmic factors and the dependence in $\varepsilon$), as $\Omega\!\left(\frac{\alpha}{d} \right)$ queries are necessary for the easier task of sampling edges from any distribution over $E$ that is close to uniform in total variational distance. We also prove that even if $G$ is a tree (i.e., $\alpha = 1$ so that $\frac{\alpha}{d}=\Theta(1)$), $\Omega\left(\frac{\log n}{\log\log n}\right)$ queries are necessary to sample an edge from any distribution that is pointwise close to uniform, thus establishing that a $\mathrm{poly}(\log n)$ factor is necessary for constant $\alpha$. Finally we show how our algorithm can be applied to obtain a new result on approximately counting subgraphs, based on the recent work of Assadi, Kapralov, and Khanna (ITCS, 2019).

Includes members:
  Member "a simple sublinear time algorithm for counting arbitrary subgraphs via edge sampling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "linear time subgraph counting graph degeneracy and the chasm at size six")
  Member "faster sublinear approximations of k cliques for low arboricity graphs":
    also connected through other hyperedges:
      (none)
  Member "lower bounds for approximating graph parameters via communication complexity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "seeing far vs seeing wide volume complexity of local graph problems")
  Member "testing bounded arboricity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improved dynamic graph coloring")
  Member "on approximating the number of k cliques in sublinear time":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "linear time subgraph counting graph degeneracy and the chasm at size six")
  Member "on sampling edges almost uniformly":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "computing and testing small connectivity in near linear time and queries via fast local cut algorithms")
  Member "sublinear time estimation of degree distribution moments the degeneracy connection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dependent randomized rounding for clustering and partition systems with knapsack constraints")
  Member "approximately counting triangles in sublinear time":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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.CC(Computational Complexity)