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 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.

Includes members:
  Member "ignorance is almost bliss near optimal stochastic matching with few queries":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stochastic matching with few queries 1 varepsilon approximation")
  Member "dynamic matching market design":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fair matching in dynamic kidney exchange")
  Member "ranking on arbitrary graphs rematch via continuous lp with monotone and boundary condition constraints":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online vertex weighted bipartite matching beating 1 1 e with random arrivals")
  Member "a stochastic probing problem with applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning optimal search algorithms from data")
  Member "maximum matching in semi streaming with few passes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "polynomial pass lower bounds for graph streaming algorithms")
  Member "when lp is the cure for your matching woes improved bounds for stochastic matchings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "predict and match prophet inequalities with uncertain supply")

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)