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: "a learning scheme for approachability in mdps and stackelberg stochastic games"
  Abstract: The notion of approachability was introduced by Blackwell in the context of vector-valued repeated games. The famous approachability theorem prescribes a strategy for approachability, i.e., for `steering' the average vector-cost of a given player towards a given target set, irrespective of the strategies of the other players. In this paper, motivated from the multi-objective optimization/decision making problems in dynamically changing environments, we address the approachability problem in Markov Decision Processes (MDPs) and Stackelberg stochastic games with vector-valued cost functions. We make two main contributions. Firstly, we give simple and computationally tractable strategy for approachability for MDPs and Stackelberg stochastic games. Secondly, we give reinforcement learning algorithms to learn the approachable strategy when the transition kernel is unknown. We also show that the conditions that we give for approachability are both necessary and sufficient for convex sets and thus a complete characterization. We also give sufficient conditions for non-convex sets.

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.LG(Machine Learning)