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: "the stochastic matching problem beating half with a non adaptive algorithm"
  Abstract: In the stochastic matching problem, we are given a general (not necessarily bipartite) graph $G(V,E)$, where each edge in $E$ is realized with some constant probability $p > 0$ and the goal is to compute a bounded-degree (bounded by a function depending only on $p$) subgraph $H$ of $G$ such that the expected maximum matching size in $H$ is close to the expected maximum matching size in $G$. The algorithms in this setting are considered non-adaptive as they have to choose the subgraph $H$ without knowing any information about the set of realized edges in $G$. Originally motivated by an application to kidney exchange, the stochastic matching problem and its variants have received significant attention in recent years. #R##N#The state-of-the-art non-adaptive algorithms for stochastic matching achieve an approximation ratio of $\frac{1}{2}-\epsilon$ for any $\epsilon > 0$, naturally raising the question that if $1/2$ is the limit of what can be achieved with a non-adaptive algorithm. In this work, we resolve this question by presenting the first algorithm for stochastic matching with an approximation guarantee that is strictly better than $1/2$: the algorithm computes a subgraph $H$ of $G$ with the maximum degree $O(\frac{\log{(1/ p)}}{p})$ such that the ratio of expected size of a maximum matching in realizations of $H$ and $G$ is at least $1/2+\delta_0$ for some absolute constant $\delta_0 > 0$. The degree bound on $H$ achieved by our algorithm is essentially the best possible (up to an $O(\log{(1/p)})$ factor) for any constant factor approximation algorithm, since an $\Omega(\frac{1}{p})$ degree in $H$ is necessary for a vertex to acquire at least one incident edge in a realization.

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"
    - "stochastic matching with few queries new algorithms and tools"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "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 "a simple 1 1 e approximation for oblivious bipartite matching"):
  with members:
    - "beating greedy for stochastic bipartite matching"
    - "perturbed greedy on oblivious matching problems"
    - "how to match when all vertices arrive online"
    - "the price of information in combinatorial optimization"
    - "online vertex weighted bipartite matching and single bid budgeted allocations"

Connected through hyperedge HE-3 (induced by source paper "beating greedy for stochastic bipartite matching"):
  with members:
    - "the price of information in combinatorial optimization"
    - "prophet secretary for combinatorial auctions and matroids"
    - "almost optimal stochastic weighted matching with few queries"
    - "stochastic packing integer programs with few queries"
    - "new algorithms better bounds and a novel model for online stochastic matching"
    - "ignorance is almost bliss near optimal stochastic matching with few queries"
    - "improved approximation algorithms for stochastic matching"
    - "the query commit problem"

Connected through hyperedge HE-4 (induced by source paper "stochastic submodular cover with limited adaptivity"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "tight bounds on the round complexity of the distributed maximum coverage problem"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "stochastic packing integer programs with few queries"
    - "scenario submodular cover"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "parallel algorithms for select and partition with noisy comparisons"
    - "ignorance is almost bliss near optimal stochastic matching with few queries"

Connected through hyperedge HE-5 (induced by source paper "towards a unified theory of sparsification for matching problems"):
  with members:
    - "stochastic matching with few queries new algorithms and tools"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "preserving distances in very faulty graphs"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "stochastic matching with few queries new algorithms and tools"):
  with members:
    - "a stochastic probing problem with applications"

Connected through hyperedge HE-7 (induced by source paper "stochastic packing integer programs with few queries"):
  with members:
    - "almost optimal stochastic weighted matching with few queries"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "ignorance is almost bliss near optimal stochastic matching with few queries"
    - "generalized hypergraph matching via iterated packing and local ratio"
    - "submodular stochastic probing on matroids"
    - "a stochastic probing problem with applications"
    - "iterative packing for demand and hypergraph matching"
    - "the query commit problem"

Connected through hyperedge HE-8 (induced by source paper "almost optimal stochastic weighted matching with few queries"):
  with members:
    - "stochastic packing integer programs with few queries"
    - "ignorance is almost bliss near optimal stochastic matching with few queries"
    - "dynamic matching market design"
    - "a stochastic probing problem with applications"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"

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)