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 "stochastic matching with few queries 1 varepsilon approximation"):
  Abstract: Suppose that we are given an arbitrary graph $G=(V, E)$ and know that each edge in $E$ is going to be realized independently with some probability $p$. The goal in the stochastic matching problem is to pick a sparse subgraph $Q$ of $G$ such that the realized edges in $Q$, in expectation, include a matching that is approximately as large as the maximum matching among the realized edges of $G$. The maximum degree of $Q$ can depend on $p$, but not on the size of $G$. #R##N#This problem has been subject to extensive studies over the years and the approximation factor has been improved from $0.5$ to $0.5001$ to $0.6568$ and eventually to $2/3$. In this work, we analyze a natural sampling-based algorithm and show that it can obtain all the way up to $(1-\epsilon)$ approximation, for any constant $\epsilon > 0$. #R##N#A key and of possible independent interest component of our analysis is an algorithm that constructs a matching on a stochastic graph, which among some other important properties, guarantees that each vertex is matched independently from the vertices that are sufficiently far. This allows us to bypass a previously known barrier towards achieving $(1-\epsilon)$ approximation based on existence of dense Ruzsa-Szemeredi graphs.

Includes members:
  Member "stochastic monotone submodular maximization with queries":
    also connected through other hyperedges:
      (none)
  Member "towards a unified theory of sparsification for matching problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rounding dynamic matchings against an adaptive adversary")
  Member "stochastic matching with few queries new algorithms and tools":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stochastic monotone submodular maximization with queries")
  Member "coresets meet edcs algorithms for matching and vertex cover on massive graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "the stochastic matching problem beating half with a non adaptive algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a simple 1 1 e approximation for oblivious bipartite matching")
  Member "almost optimal stochastic weighted matching with few queries":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed weighted matching via randomized composable coresets")
  Member "opting into optimal matchings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to screen")
  Member "ignorance is almost bliss near optimal stochastic matching with few queries":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed weighted matching via randomized composable coresets")

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)