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 "beating greedy for stochastic bipartite matching"):
  Abstract: We consider the maximum bipartite matching problem in stochastic settings, namely the query-commit and price-of-information models. In the query-commit model, an edge e independently exists with probability pe. We can query whether an edge exists or not, but if it does exist, then we have to take it into our solution. In the unweighted case, one can query edges in the order given by the classical online algorithm of Karp, Vazirani, and Vazirani [20] to get a (1 − 1/e)-approximation. In contrast, the previously best known algorithm in the weighted case is the (1/2)-approximation achieved by the greedy algorithm that sorts the edges according to their weights and queries in that order.#R##N##R##N#Improving upon the basic greedy, we give a (1 -1/e)-approximation algorithm in the weighted query-commit model. We use a linear program (LP) to upper bound the optimum achieved by any strategy. The proposed LP admits several structural properties that play a crucial role in the design and analysis of our algorithm. We also extend these techniques to get a (1 − 1/e)-approximation algorithm for maximum bipartite matching in the price-of-information model introduced by Singla [25], who also used the basic greedy algorithm to give a (1/2)-approximation.

Includes members:
  Member "the price of information in combinatorial optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pandora s box problem with order constraints")
  Member "the stochastic matching problem beating half with a non adaptive algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stochastic matching with few queries 1 varepsilon approximation")
  Member "prophet secretary for combinatorial auctions and matroids":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "prophet inequalities with linear correlations and augmentations")
  Member "almost optimal stochastic weighted matching with few queries":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "stochastic submodular cover with limited adaptivity")
  Member "new algorithms better bounds and a novel model for online stochastic matching":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online graph matching problems with a worst case reassignment budget")
  Member "ignorance is almost bliss near optimal stochastic matching with few queries":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stochastic matching with few queries 1 varepsilon approximation")
  Member "improved approximation algorithms for stochastic matching":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the markovian price of information")

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)