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: "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.

Connected through hyperedge HE-1 (induced by source paper "near optimal exploration exploitation in non communicating markov decision processes"):
  with members:
    - "regal a regularization based algorithm for reinforcement learning in weakly communicating mdps"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

Connected through hyperedge HE-2 (induced by source paper "extreme state aggregation beyond mdps"):
  with members:
    - "the sample complexity of general reinforcement learning"
    - "selecting the state representation in reinforcement learning"
    - "metrics for finite markov decision processes"
    - "pac bounds for discounted mdps"
    - "feature reinforcement learning in practice"
    - "consistency of feature markov processes"
    - "feature reinforcement learning part i unstructured mdps"
    - "feature dynamic bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "selecting near optimal approximate state representations in reinforcement learning"):
  with members:
    - "online regret bounds for undiscounted continuous reinforcement learning"
    - "regal a regularization based algorithm for reinforcement learning in weakly communicating mdps"
    - "feature reinforcement learning part i unstructured mdps"

Connected through hyperedge HE-4 (induced by source paper "the sample complexity of general reinforcement learning"):
  with members:
    - "on the sample complexity of reinforcement learning with a generative model"
    - "pac bounds for discounted mdps"
    - "optimistic agents are asymptotically optimal"
    - "asymptotically optimal agents"
    - "time consistent discounting"
    - "on the possibility of learning in reactive environments with arbitrary dependence"
    - "self optimizing and pareto optimal policies in general environments based on bayes mixtures"

Connected through hyperedge HE-5 (induced by source paper "unsupervised model free representation learning"):
  with members:
    - "selecting the state representation in reinforcement learning"
    - "methods for computing state similarity in markov decision processes"
    - "consistency of feature markov processes"
    - "feature reinforcement learning part i unstructured mdps"
    - "universal codes as a basis for time series testing"
    - "pattern recognition for conditionally independent data"
    - "using information theory approach to randomness testing"

Connected through hyperedge HE-6 (induced by source paper "online regret bounds for undiscounted continuous reinforcement learning"):
  with members:
    - "selecting the state representation in reinforcement learning"
    - "regret bounds for restless markov bandits"
    - "regal a regularization based algorithm for reinforcement learning in weakly communicating mdps"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-7 (induced by source paper "regret bounds for restless markov bandits"):
  with members:
    - "online regret bounds for undiscounted continuous reinforcement learning"
    - "selecting the state representation in reinforcement learning"
    - "regal a regularization based algorithm for reinforcement learning in weakly communicating mdps"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "on the possibility of learning in reactive environments with arbitrary dependence"

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)