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 "testing graph clusterability algorithms and lower bounds"):
  Abstract: We consider the problem of testing graph cluster structure: given access to a graph $G=(V, E)$, can we quickly determine whether the graph can be partitioned into a few clusters with good inner conductance, or is far from any such graph? This is a generalization of the well-studied problem of testing graph expansion, where one wants to distinguish between the graph having good expansion (i.e.\ being a good single cluster) and the graph having a sparse cut (i.e.\ being a union of at least two clusters). A recent work of Czumaj, Peng, and Sohler (STOC'15) gave an ingenious sublinear time algorithm for testing $k$-clusterability in time $\tilde{O}(n^{1/2} \text{poly}(k))$: their algorithm implicitly embeds a random sample of vertices of the graph into Euclidean space, and then clusters the samples based on estimates of Euclidean distances between the points. This yields a very efficient testing algorithm, but only works if the cluster structure is very strong: it is necessary to assume that the gap between conductances of accepted and rejected graphs is at least logarithmic in the size of the graph $G$. In this paper we show how one can leverage more refined geometric information, namely angles as opposed to distances, to obtain a sublinear time tester that works even when the gap is a sufficiently large constant. Our tester is based on the singular value decomposition of a natural matrix derived from random walk transition probabilities from a small sample of seed nodes. #R##N#We complement our algorithm with a matching lower bound on the query complexity of testing clusterability. Our lower bound is based on a novel property testing problem, which we analyze using Fourier analytic tools. As a byproduct of our techniques, we also achieve new lower bounds for the problem of approximating MAX-CUT value in sublinear time.

Includes members:
  Member "testing bounded arboricity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "linear time subgraph counting graph degeneracy and the chasm at size six")
  Member "estimating graph parameters from random order streams":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism")
  Member "on sampling edges almost uniformly":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "dependent randomized rounding for clustering and partition systems with knapsack constraints")
  Member "testing cluster structure of graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "walking randomly massively and efficiently")
  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")
  Member "a simpler sublinear algorithm for approximating the triangle count":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "slim graph practical lossy graph compression for approximate graph processing storage and analytics")

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.DS(Data Structures and Algorithms)