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 "on the possibility of learning in reactive environments with arbitrary dependence"):
  Abstract: We address the problem of reinforcement learning in which observations may exhibit an arbitrary form of stochastic dependence on past observations and actions, i.e. environments more general than (PO)MDPs. The task for an agent is to attain the best possible asymptotic reward where the true generating environment is unknown but belongs to a known countable family of environments. We find some sufficient conditions on the class of environments under which an agent exists which attains the best asymptotic reward for any environment in the class. We analyze how tight these conditions are and how they relate to different probabilistic assumptions known in reinforcement learning and related fields, such as Markov Decision Processes and mixing conditions.

Includes members:
  Member "on sequence prediction for arbitrary measures":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "offline to online conversion")
  Member "general discounting versus average reward":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "thompson sampling is asymptotically optimal in general environments")
  Member "defensive universal learning with experts":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "open problems in universal induction intelligence")
  Member "universal learning of repeated matrix games":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "one decade of universal artificial intelligence")
  Member "prediction with expert advice by following the perturbed leader for general weights":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "first order regret bounds for combinatorial semi bandits")
  Member "optimality of universal bayesian sequence prediction for general loss and alphabet":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "thompson sampling for adversarial bit prediction")
  Member "self optimizing and pareto optimal policies in general environments based on bayes mixtures":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "probabilistically safe policy transfer")

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.LG(Machine Learning)