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: "stochastic matching with few queries new algorithms and tools"
  Abstract: We consider the following stochastic matching problem on both weighted and unweighted graphs: A graph $G(V, E)$ along with a parameter $p \in (0, 1)$ is given in the input. Each edge of $G$ is realized independently with probability $p$. The goal is to select a degree bounded (dependent only on $p$) subgraph $H$ of $G$ such that the expected size/weight of maximum realized matching of $H$ is close to that of $G$. #R##N#This model of stochastic matching has attracted significant attention over the recent years due to its various applications. The most fundamental open question is the best approximation factor achievable for such algorithms that, in the literature, are referred to as non-adaptive algorithms. Prior work has identified breaking (near) half-approximation as a barrier for both weighted and unweighted graphs. Our main results are as follows: #R##N#-- We analyze a simple and clean algorithm and show that for unweighted graphs, it finds an (almost) $4\sqrt{2}-5$ ($\approx 0.6568$) approximation by querying $O(\frac{\log (1/p)}{p})$ edges per vertex. This improves over the state-of-the-art $0.5001$ approximate algorithm of Assadi et al. [EC'17]. #R##N#-- We show that the same algorithm achieves a $0.501$ approximation for weighted graphs by querying $O(\frac{\log (1/p)}{p})$ edges per vertex. This is the first algorithm to break $0.5$ approximation barrier for weighted graphs. It also improves the per-vertex queries of the state-of-the-art by Yamaguchi and Maehara [SODA'18] and Behnezhad and Reyhani [EC'18]. #R##N#Our algorithms are fundamentally different from prior works, yet are very simple and natural. For the analysis, we introduce a number of procedures that construct heavy fractional matchings. We consider the new algorithms and our analytical tools to be the main contributions of this paper.

Connected through hyperedge HE-1 (induced by source paper "stochastic matching with few queries 1 varepsilon approximation"):
  with members:
    - "stochastic monotone submodular maximization with queries"
    - "towards a unified theory of sparsification for matching problems"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "the stochastic matching problem beating half with a non adaptive algorithm"
    - "almost optimal stochastic weighted matching with few queries"
    - "opting into optimal matchings"
    - "ignorance is almost bliss near optimal stochastic matching with few queries"
    - "better bounds for matchings in the streaming model"

Connected through hyperedge HE-2 (induced by source paper "stochastic monotone submodular maximization with queries"):
  with members:
    - "almost optimal stochastic weighted matching with few queries"
    - "prophet inequalities made easy stochastic optimization by pricing non stochastic inputs"
    - "ignorance is almost bliss near optimal stochastic matching with few queries"

Connected through hyperedge HE-3 (induced by source paper "towards a unified theory of sparsification for matching problems"):
  with members:
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "preserving distances in very faulty graphs"
    - "the stochastic matching problem beating half with a non adaptive algorithm"
    - "on estimating maximum matching size in graph streams"
    - "almost optimal stochastic weighted matching with few queries"
    - "stochastic packing integer programs with few queries"
    - "ignorance is almost bliss near optimal stochastic matching with few queries"
    - "fully dynamic matching in bipartite 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.DS(Data Structures and Algorithms)