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 "distributionally robust optimization for sequential decision making"):
  Abstract: The distributionally robust Markov Decision Process approach has been proposed in the literature, where the goal is to seek a distributionally robust policy that achieves the maximal expected total reward under the most adversarial joint distribution of uncertain parameters. In this paper, we study distributionally robust MDP where ambiguity sets for uncertain parameters are of a format that can easily incorporate in its description the uncertainty's statistical information estimated from historical data. In this way, we generalize existing works on distributionally robust Markov Decision Process with generalized-moment-based ambiguity sets and statistical-distance-based ambiguity sets to incorporate information from the former class such as moments and dispersions to the latter class that critically depend on samples. We show that, under this format of ambiguity sets, the resulting distributionally robust Markov Decision Process remains tractable under mild technical conditions. To be more specific, a distributionally robust policy can be constructed by solving a collection of one-stage convex optimization subproblems.

Includes members:
  Member "approximate value iteration for risk aware markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "model and algorithm for time consistent risk aware markov games")
  Member "distributionally robust counterpart in markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a bayesian approach to robust reinforcement learning")

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.SY(Systems and Control)