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: "learning to control in metric space with optimal regret"
  Abstract: We study online reinforcement learning for finite-horizon deterministic control systems with {\it arbitrary} state and action spaces. Suppose that the transition dynamics and reward function is unknown, but the state and action space is endowed with a metric that characterizes the proximity between different states and actions. We provide a surprisingly simple upper-confidence reinforcement learning algorithm that uses a function approximation oracle to estimate optimistic Q functions from experiences. We show that the regret of the algorithm after $K$ episodes is $O(HL(KH)^{\frac{d-1}{d}}) $ where $L$ is a smoothness parameter, and $d$ is the doubling dimension of the state-action space with respect to the given metric. We also establish a near-matching regret lower bound. The proposed method can be adapted to work for more structured transition systems, including the finite-state case and the case where value functions are linear combinations of features, where the method also achieve the optimal regret.

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"
    - "efficient model free reinforcement learning in metric spaces"
    - "non asymptotic gap dependent regret bounds for tabular mdps"

Connected through hyperedge HE-2 (induced by source paper "q learning for mean field controls"):
  with members:
    - "multi agent reinforcement learning a selective overview of theories and algorithms"
    - "natural actor critic converges globally for hierarchical linear quadratic regulator"
    - "model based reinforcement learning for atari"
    - "an algorithmic perspective on imitation learning"
    - "q learning with nearest neighbors"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "practical kernel based reinforcement learning"
    - "efficient reinforcement learning in deterministic systems with value function generalization"

Connected through hyperedge HE-3 (induced by source paper "zooming for efficient model free reinforcement learning in metric spaces"):
  with members:
    - "lipschitz continuity in model based reinforcement learning"
    - "is q learning provably efficient"
    - "online regret bounds for undiscounted continuous reinforcement learning"
    - "the sample complexity of general reinforcement learning"
    - "metrics for finite markov decision processes"
    - "contextual bandits with similarity information"

Connected through hyperedge HE-4 (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"
    - "efficient model free reinforcement learning in metric spaces"
    - "non asymptotic gap dependent regret bounds for tabular mdps"
    - "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-5 (induced by source paper "adaptive discretization for episodic reinforcement learning in metric spaces"):
  with members:
    - "sample optimal parametric q learning using linearly additive features"
    - "efficient model free reinforcement learning in metric spaces"
    - "non asymptotic gap dependent regret bounds for tabular mdps"
    - "nonparametric contextual bandits in an unknown metric space"
    - "stochastic lipschitz q learning"
    - "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"

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)