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 "optimal regret bounds for selecting the state representation in reinforcement learning"):
  Abstract: We consider an agent interacting with an environment in a single stream of actions, observations, and rewards, with no reset. This process is not assumed to be a Markov Decision Process (MDP). Rather, the agent has several representations (mapping histories of past interactions to a discrete state space) of the environment with unknown dynamics, only some of which result in an MDP. The goal is to minimize the average regret criterion against an agent who knows an MDP representation giving the highest optimal reward, and acts optimally in it. Recent regret bounds for this setting are of order $O(T^{2/3})$ with an additive term constant yet exponential in some characteristics of the optimal MDP. We propose an algorithm whose regret after $T$ time steps is $O(\sqrt{T})$, with all constants reasonably small. This is optimal in $T$ since $O(\sqrt{T})$ is the optimal regret in the setting of learning in a (single discrete) MDP.

Includes members:
  Member "online regret bounds for undiscounted continuous reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "regime switching bandits")
  Member "selecting the state representation in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning representations in model free hierarchical reinforcement learning")
  Member "predictive state representations a new theory for modeling dynamical systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discovery of useful questions as auxiliary tasks")
  Member "regal a regularization based algorithm for reinforcement learning in weakly communicating mdps":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning near optimal policies with low inherent bellman error")
  Member "feature reinforcement learning part i unstructured mdps":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a geometric perspective on optimal representations for reinforcement learning")
  Member "a monte carlo aixi approximation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "influence aware memory for deep reinforcement learning")
  Member "on the possibility of learning in reactive environments with arbitrary dependence":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning and planning for time varying mdps using maximum likelihood estimation")

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)