human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "online primal dual meets online matching with stochastic rewards configuration lp to the rescue"):
  with members:
    - "online matching with stochastic rewards optimal competitive ratio via path based formulation"
    - "tight competitive ratios of classic matching algorithms in the fully online model"
    - "how to match when all vertices arrive online"
    - "maximum weight online matching with deadlines"
    - "online vertex weighted bipartite matching beating 1 1 e with random arrivals"
    - "online vertex weighted bipartite matching and single bid budgeted allocations"
    - "online stochastic matching online actions based on offline statistics"
    - "online stochastic matching beating 1 1 e"

Connected through hyperedge HE-2 (induced by source paper "online matching with stochastic rewards optimal competitive ratio via path based formulation"):
  with members:
    - "online allocation of reusable resources achieving optimal competitive ratio"
    - "online primal dual meets online matching with stochastic rewards configuration lp to the rescue"
    - "new algorithms better bounds and a novel model for online stochastic matching"
    - "budget optimal task allocation for reliable crowdsourcing systems"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"
    - "online stochastic matching online actions based on offline statistics"
    - "online stochastic matching beating 1 1 e"
    - "algorithms for secretary problems on graphs and hypergraphs"

Question: Please tell me which class the center node 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)