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: "allocation problems in ride sharing platforms online matching with offline reusable resources"
  Abstract: Bipartite matching markets pair agents on one side of a market with agents, items, or contracts on the opposing side. Prior work addresses online bipartite matching markets, where agents arrive over time and are dynamically matched to a known set of disposable resources. In this paper, we propose a new model, Online Matching with (offline) Reusable Resources under Known Adversarial Distributions (OM-RR-KAD), in which resources on the offline side are reusable instead of disposable; that is, once matched, resources become available again at some point in the future. We show that our model is tractable by presenting an LP-based adaptive algorithm that achieves an online competitive ratio of 1/2 - eps for any given eps greater than 0. We also show that no non-adaptive algorithm can achieve a ratio of 1/2 + o(1) based on the same benchmark LP. Through a data-driven analysis on a massive openly-available dataset, we show our model is robust enough to capture the application of taxi dispatching services and ride-sharing systems. We also present heuristics that perform well in practice.

Connected through hyperedge HE-1 (induced by source paper "online policies for efficient volunteer crowdsourcing"):
  with members:
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "bayesian combinatorial auctions expanding single buyer mechanisms to many buyers"
    - "budget optimal task allocation for reliable crowdsourcing systems"
    - "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:
    - "online matching with stochastic rewards optimal competitive ratio via path based formulation"
    - "algorithms for online matching assortment and pricing with tight weight dependent competitive ratios"
    - "budget optimal task allocation for reliable crowdsourcing systems"
    - "online stochastic matching beating 1 1 e"

Connected through hyperedge HE-3 (induced by source paper "putting ridesharing to the test efficient and scalable solutions and the power of dynamic vehicle relocation"):
  with members:
    - "shared autonomous vehicle simulation and service design"
    - "dynamic trip vehicle dispatch with scheduled and on demand requests"
    - "optimization of ride sharing systems using event driven receding horizon control"
    - "anytime heuristic for weighted matching through altruism inspired behavior"
    - "the online k taxi problem"
    - "a primal dual online deterministic algorithm for matching with delays"
    - "spatio temporal pricing for ridesharing platforms"
    - "stochastic k server how should uber work"

Connected through hyperedge HE-4 (induced by source paper "mix and match markov chains mixing times for matching in rideshare"):
  with members:
    - "minimizing latency in online ride and delivery services"
    - "spatio temporal pricing for ridesharing platforms"
    - "efficient large scale fleet management via multi agent deep reinforcement learning"
    - "deep reinforcement learning an overview"
    - "pricing and optimization in shared vehicle systems an approximation framework"
    - "rapidly mixing markov chains a comparison of techniques a survey"
    - "an optimization framework for online ride sharing markets"

Connected through hyperedge HE-5 (induced by source paper "balancing the tradeoff between profit and fairness in rideshare platforms during high demand hours"):
  with members:
    - "spatio temporal pricing for ridesharing platforms"
    - "attenuate locally win globally an attenuation based framework for online stochastic matching with timeouts"
    - "fair allocation of indivisible public goods"
    - "pricing and optimization in shared vehicle systems an approximation framework"
    - "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.AI(Artificial Intelligence)