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: "selecting near optimal approximate state representations in reinforcement learning"
  Abstract: We consider a reinforcement learning setting introduced in (Maillard et al., NIPS 2011) where the learner does not have explicit access to the states of the underlying Markov decision process (MDP). Instead, she has access to several models that map histories of past interactions to states. Here we improve over known regret bounds in this setting, and more importantly generalize to the case where the models given to the learner do not contain a true model resulting in an MDP representation but only approximations of it. We also give improved error bounds for state aggregation.

Connected through hyperedge HE-1 (induced by source paper "provably efficient rl with rich observations via latent state decoding"):
  with members:
    - "is q learning provably efficient"
    - "value prediction network"
    - "count based exploration with neural density models"
    - "curiosity driven exploration by self supervised prediction"
    - "reinforcement learning in rich observation mdps using spectral methods"
    - "deep exploration via bootstrapped dqn"
    - "reinforcement learning of pomdps using spectral methods"
    - "contextual decision processes with low bellman rank are pac learnable"

Connected through hyperedge HE-2 (induced by source paper "model based reinforcement learning exploiting state action equivalence"):
  with members:
    - "efficient bias span constrained exploration exploitation in reinforcement learning"
    - "near optimal behavior via approximate state abstraction"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "sample complexity of multi task reinforcement learning"
    - "model reduction techniques for computing approximately optimal solutions for markov decision processes"
    - "metrics for finite markov decision processes"
    - "regal a regularization based algorithm for reinforcement learning in weakly communicating mdps"

Connected through hyperedge HE-3 (induced by source paper "variational regret bounds for reinforcement learning"):
  with members:
    - "a new algorithm for non stationary contextual bandits efficient optimal and parameter free"
    - "regret bounds for restless markov bandits"
    - "regal a regularization based algorithm for reinforcement learning in weakly communicating mdps"

Connected through hyperedge HE-4 (induced by source paper "sample efficient feature selection for factored mdps"):
  with members:
    - "playing atari with deep reinforcement learning"
    - "greedy algorithms for sparse reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "reinforcement learning of pomdps using spectral methods"):
  with members:
    - "a pac rl algorithm for episodic pomdps"
    - "online learning and optimization of markov jump affine models"
    - "efficient learning and planning with compressed predictive states"
    - "nonparametric estimation of multi view latent variable models"
    - "on learning parametric output hmms"
    - "pegasus a policy search method for large mdps and pomdps"
    - "tensor decompositions for learning latent variable models"
    - "a method of moments for mixture models and hidden markov models"

Connected through hyperedge HE-6 (induced by source paper "reinforcement learning of pomdp s using spectral methods"):
  with members:
    - "a pac rl algorithm for episodic pomdps"
    - "contextual mdps for pac reinforcement learning with rich observations"
    - "online learning and optimization of markov jump affine models"
    - "nonparametric estimation of multi view latent variable models"
    - "on learning parametric output hmms"
    - "pegasus a policy search method for large mdps and pomdps"
    - "tensor decompositions for learning latent variable models"
    - "a method of moments for mixture models and hidden markov models"

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)