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 "almost optimal stochastic weighted matching with few queries"):
  Abstract: We consider the stochastic matching problem. An edge-weighted general graph $G(V, E)$ is given in the input, where each edge in $E$ is realized independently with probability $p$. The realization is initially unknown, however, we are able to query the edges to determine whether they are realized. The goal is to query only a small number of edges to find a realized matching that is sufficiently close to the optimum (i.e., the maximum matching among all realized edges). #R##N#The stochastic matching problem has been studied extensively during the past decade because of its numerous real-world applications in kidney-exchange, matchmaking services, online labor markets, advertisement, etc. #R##N#Our main result is an adaptive algorithm that, in expectation, finds a $(1-\epsilon)$-approximation by querying only $O(1)$ edges per vertex. Prior to our work, no nontrivial approximation was known for weighted graphs using a constant per-vertex budget. The only known result for weighted graphs is the algorithm of Maehara and Yamaguchi~(SODA'18) that achieves a $(1-\epsilon)$-approximation by querying $\Theta(w\log{n})$ edges per vertex where $w$ is the maximum edge-weight. Our result is a substantial improvement over this bound and has an appealing practical message: No matter what the structure of the input graph is, one can get arbitrarily close to the optimum solution by querying only a constant number of edges per vertex. #R##N#To obtain our results, we introduce novel properties of weighted augmenting paths that may be of independent interest in generalizing augmenting path based techniques to weighted graphs.

Includes members:
  Member "the stochastic matching problem beating half with a non adaptive algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stochastic matching with few queries 1 varepsilon approximation")
  Member "stochastic packing integer programs with few queries":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "beating greedy for stochastic bipartite matching")
  Member "ignorance is almost bliss near optimal stochastic matching with few queries":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stochastic matching with few queries 1 varepsilon approximation")
  Member "dynamic matching market design":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fair matching in dynamic kidney exchange")
  Member "a stochastic probing problem with applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning optimal search algorithms from data")
  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)