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: "efficient model free reinforcement learning in metric spaces"
  Abstract: Model-free Reinforcement Learning (RL) algorithms such as Q-learning [Watkins, Dayan 92] have been widely used in practice and can achieve human level performance in applications such as video games [Mnih et al. 15]. Recently, equipped with the idea of optimism in the face of uncertainty, Q-learning algorithms [Jin, Allen-Zhu, Bubeck, Jordan 18] can be proven to be sample efficient for discrete tabular Markov Decision Processes (MDPs) which have finite number of states and actions. In this work, we present an efficient model-free Q-learning based algorithm in MDPs with a natural metric on the state-action space--hence extending efficient model-free Q-learning algorithms to continuous state-action space. Compared to previous model-based RL algorithms for metric spaces [Kakade, Kearns, Langford 03], our algorithm does not require access to a black-box planning oracle.

Connected through hyperedge HE-1 (induced by source paper "provably efficient exploration for rl with unsupervised learning"):
  with members:
    - "agnostic q learning with function approximation in deterministic systems tight bounds on approximation error and sample complexity"
    - "provably efficient rl with rich observations via latent state decoding"
    - "provably efficient reinforcement learning with linear function approximation"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "sample optimal parametric q learning using linearly additive features"
    - "a theoretical analysis of deep q learning"
    - "non asymptotic gap dependent regret bounds for tabular mdps"
    - "learning to control in metric space with optimal regret"

Connected through hyperedge HE-2 (induced by source paper "provably efficient q learning with function approximation via distribution shift error checking oracle"):
  with members:
    - "provably efficient rl with rich observations via latent state decoding"
    - "finite sample analysis for sarsa and q learning with linear function approximation"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "non asymptotic gap dependent regret bounds for tabular mdps"
    - "learning to control in metric space with optimal regret"
    - "on exact computation with an infinitely wide neural net"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "on polynomial time pac reinforcement learning with rich observations"

Connected through hyperedge HE-3 (induced by source paper "adaptive discretization for episodic reinforcement learning in metric spaces"):
  with members:
    - "sample optimal parametric q learning using linearly additive features"
    - "non asymptotic gap dependent regret bounds for tabular mdps"
    - "nonparametric contextual bandits in an unknown metric space"
    - "stochastic lipschitz q learning"
    - "learning to control in metric space with optimal regret"
    - "provably efficient q learning with function approximation via distribution shift error checking oracle"
    - "q learning with ucb exploration is sample efficient for infinite horizon mdp"
    - "reinforcement leaning in feature space matrix bandit kernels and regret bound"

Connected through hyperedge HE-4 (induced by source paper "non asymptotic gap dependent regret bounds for tabular mdps"):
  with members:
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "is q learning provably efficient"
    - "policy certificates towards accountable reinforcement learning"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "regal a regularization based algorithm for reinforcement learning in weakly communicating mdps"

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)