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 "vertex weighted online stochastic matching with patience constraints"):
  Abstract: Online Bipartite Matching is a classic problem introduced by Karp, Vazirani, and Vazirani (Proc. ACM STOC, 1990) and motivated by applications such as e-commerce, online advertising, and ride-sharing. We wish to match a set of online vertices (e.g., webpage views, users, customers, or riders) which arrive sequentially one-by-one to a set of offline vertices (e.g., ads, items, or drivers). Each vertex can be matched at most once and the goal is to maximize the matching size. #R##N#Here, we consider the more general problem allowing vertex weights (on the offline vertices, representing differing item prices, for example), stochastic rewards (attempted matches may be unsuccessful, providing no reward), and patience constraints (each online vertex has a patience, representing the maximum number of unsuccessful match attempts allowed for it). In doing so, we discuss and clarify the definition of competitive ratio in the stochastic setting and compare competing notions. Specifically, we advocate for the use of a competitive ratio that compares the performance of an online algorithm to that of an offline algorithm in the same stochastic setting, whereas some prior work---see, e.g., Mehta and Panigrahi (FOCS, 2012)---uses a linear programming (LP) formulation to compare the performance of an online algorithm to the optimal solution of a closely related non-stochastic offline problem. We define the stochasticity gap for evaluating the usefulness of an LP formulation (which exchanges probabilities for fractional coefficients) of a given stochastic problem and bound this gap for a commonly used LP. We further present and analyze a new algorithm for our problem, achieving a competitive ratio of 0.5, the first constant competitive ratio for this and several related problems.

Includes members:
  Member "online matching with stochastic rewards optimal competitive ratio via path based formulation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online primal dual meets online matching with stochastic rewards configuration lp to the rescue")
  Member "greedy bipartite matching in random type poisson arrival model":
    also connected through other hyperedges:
      (none)
  Member "attenuate locally win globally an attenuation based framework for online stochastic matching with timeouts":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "balancing the tradeoff between profit and fairness in rideshare platforms during high demand hours")
  Member "an experimental study of algorithms for online bipartite matching":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on conceptually simple algorithms for variants of online bipartite matching")
  Member "online vertex weighted bipartite matching beating 1 1 e with random arrivals":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online graph matching problems with a worst case reassignment budget")
  Member "new algorithms better bounds and a novel model for online stochastic matching":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online graph matching problems with a worst case reassignment budget")
  Member "improved approximation algorithms for stochastic matching":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the markovian price of information")
  Member "greedy online bipartite matching on random graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an experimental study of algorithms for online bipartite matching")

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)