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 "online primal dual meets online matching with stochastic rewards configuration lp to the rescue"):
  Abstract: Mehta and Panigrahi (FOCS 2012) introduce the problem of online matching with stochastic rewards, where edges are associated with success probabilities and a match succeeds with the probability of the corresponding edge. It is one of the few online matching problems that have defied the randomized online primal dual framework by Devanur, Jain, and Kleinberg (SODA 2013) thus far. This paper unlocks the power of randomized online primal dual in online matching with stochastic rewards by employing the configuration linear program rather than the standard matching linear program used in previous works. Our main result is a 0.572 competitive algorithm for the case of vanishing and unequal probabilities, improving the best previous bound of 0.534 by Mehta, Waggoner, and Zadimoghaddam (SODA 2015) and, in fact, is even better than the best previous bound of 0.567 by Mehta and Panigrahi (FOCS 2012) for the more restricted case of vanishing and equal probabilities. For vanishing and equal probabilities, we get a better competitive ratio of 0.576. Our results further generalize to the vertex-weighted case due to the intrinsic robustness of the randomized online primal dual analysis.

Includes members:
  Member "vertex weighted online stochastic matching with patience constraints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online matching with stochastic rewards optimal competitive ratio via path based formulation")
  Member "online matching with stochastic rewards optimal competitive ratio via path based formulation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online allocation of reusable resources achieving optimal competitive ratio")
  Member "tight competitive ratios of classic matching algorithms in the fully online model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online stochastic max weight matching prophet inequality for vertex and edge arrival models")
  Member "how to match when all vertices arrive online":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online stochastic max weight matching prophet inequality for vertex and edge arrival models")
  Member "maximum weight online matching with deadlines":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "anytime heuristic for weighted matching through altruism inspired behavior")
  Member "online vertex weighted bipartite matching beating 1 1 e with random arrivals":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online graph matching problems with a worst case reassignment budget")
  Member "online vertex weighted bipartite matching and single bid budgeted allocations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "online graph matching problems with a worst case reassignment budget")
  Member "online stochastic matching online actions based on offline statistics":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "online graph matching problems with a worst case reassignment budget")

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)