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: "online matching with stochastic rewards optimal competitive ratio via path based formulation"
  Abstract: The problem of online matching with stochastic rewards is a variant of the online bipartite matching problem where each edge has a probability of "success". When a match is made it succeeds with the probability of the corresponding edge. Introducing this model, Mehta and Panigrahi (FOCS 2012) focused on the special case of identical and vanishingly small edge probabilities and gave an online algorithm which is 0.567 competitive against a deterministic offline LP. For the case of vanishingly small but heterogeneous probabilities Mehta et al. (SODA 2015), gave a 0.534 competitive algorithm against the same LP benchmark. #R##N#We study a generalization of the problem to vertex-weighted graphs and compare against clairvoyant algorithms that know the sequence of arrivals and the edge probabilities in advance, but not the outcomes of potential matches. To the best of our knowledge, no results beating $1/2$ were previously known for this setting, even for identical probabilities. By introducing a novel path-based formulation, we show that a natural variant of the RANKING algorithm achieves the best possible competitive ratio of $(1-1/e)$, for heterogeneous but vanishingly small edge probabilities. Our result also holds for non-vanishing probabilities that decompose as a product of two factors, one corresponding to each vertex of the edge. The idea of a path-based program may be of independent interest in other online matching problems with a stochastic component.

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:
    - "vertex weighted online stochastic matching with patience constraints"
    - "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 allocation of reusable resources achieving optimal competitive ratio"):
  with members:
    - "algorithms for online matching assortment and pricing with tight weight dependent competitive ratios"
    - "allocation problems in ride sharing platforms online matching with offline reusable resources"
    - "budget optimal task allocation for reliable crowdsourcing systems"
    - "online stochastic matching beating 1 1 e"

Connected through hyperedge HE-3 (induced by source paper "vertex weighted online stochastic matching with patience constraints"):
  with members:
    - "greedy bipartite matching in random type poisson arrival model"
    - "attenuate locally win globally an attenuation based framework for online stochastic matching with timeouts"
    - "an experimental study of algorithms for online bipartite matching"
    - "online vertex weighted bipartite matching beating 1 1 e with random arrivals"
    - "new algorithms better bounds and a novel model for online stochastic matching"
    - "improved approximation algorithms for stochastic matching"
    - "greedy online bipartite matching on random graphs"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"

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)