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: "a pac rl algorithm for episodic pomdps"
  Abstract: Many interesting real world domains involve reinforcement learning (RL) in partially observable environments. Efficient learning in such domains is important, but existing sample complexity bounds for partially observable RL are at least exponential in the episode length. We give, to our knowledge, the first partially observable RL algorithm with a polynomial bound on the number of episodes on which the algorithm may not achieve near-optimal performance. Our algorithm is suitable for an important class of episodic POMDPs. Our approach builds on recent advances in method of moments for latent variable model estimation.

Connected through hyperedge HE-1 (induced by source paper "on oracle efficient pac rl with rich observations"):
  with members:
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "reinforcement learning in rich observation mdps using spectral methods"
    - "reinforcement learning of pomdps using spectral methods"
    - "contextual decision processes with low bellman rank are pac learnable"
    - "pac reinforcement learning with rich observations"
    - "learning to search better than your teacher"
    - "reinforcement and imitation learning via interactive no regret learning"

Connected through hyperedge HE-2 (induced by source paper "on polynomial time pac reinforcement learning with rich observations"):
  with members:
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "reinforcement learning in rich observation mdps using spectral methods"
    - "reinforcement learning of pomdps using spectral methods"
    - "contextual decision processes with low bellman rank are pac learnable"
    - "pac reinforcement learning with rich observations"
    - "learning to search better than your teacher"
    - "reinforcement and imitation learning via interactive no regret learning"
    - "taming the monster a fast and simple algorithm for contextual bandits"

Connected through hyperedge HE-3 (induced by source paper "hidden markov model estimation based q learning for partially observable markov decision process"):
  with members:
    - "qmdp net deep learning for planning under partial observability"
    - "deep recurrent q learning for partially observable mdps"
    - "memory based control with recurrent neural networks"
    - "discretized approximations for pomdp with average cost"

Connected through hyperedge HE-4 (induced by source paper "reinforcement learning in rich observation mdps using spectral methods"):
  with members:
    - "experimental results reinforcement learning of pomdps using spectral methods"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "reinforcement learning of pomdp s using spectral methods"
    - "contextual mdps for pac reinforcement learning with rich observations"
    - "open problem approximate planning of pomdps in the class of memoryless policies"
    - "reinforcement learning of pomdps using spectral methods"
    - "contextual decision processes with low bellman rank are pac learnable"
    - "pac reinforcement learning with rich observations"

Connected through hyperedge HE-5 (induced by source paper "reinforcement learning of pomdps using spectral methods"):
  with members:
    - "online learning and optimization of markov jump affine models"
    - "selecting near optimal approximate state representations in reinforcement learning"
    - "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:
    - "contextual mdps for pac reinforcement learning with rich observations"
    - "online learning and optimization of markov jump affine models"
    - "selecting near optimal approximate state representations in reinforcement learning"
    - "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)