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: "q learning with nearest neighbors"
  Abstract: We consider the problem of model-free reinforcement learning for infinite-horizon discounted Markov Decision Processes (MDPs) with a continuous state space and unknown transition kernels, when only a single sample path of the system is available. We focus on the classical approach of Q-learning where the goal is to learn the optimal Q-function. We propose the Nearest Neighbor Q-Learning approach that utilizes nearest neighbor regression method to learn the Q function. We provide finite sample analysis of the convergence rate using this method. In particular, we establish that the algorithm is guaranteed to output an $\epsilon$-accurate estimate of the optimal Q-function with high probability using a number of observations that depends polynomially on $\epsilon$ and the model parameters. To establish our results, we develop a robust version of stochastic approximation results; this may be of interest in its own right.

Connected through hyperedge HE-1 (induced by source paper "option discovery in the absence of rewards with manifold analysis"):
  with members:
    - "the laplacian in rl learning representations with efficient approximations"
    - "spectral embedding norm looking deep into the spectrum of the graph laplacian"
    - "diversity is all you need learning skills without a reward function"
    - "eigenoption discovery through the deep successor representation"
    - "curiosity driven exploration by self supervised prediction"
    - "stochastic neural networks for hierarchical reinforcement learning"
    - "a laplacian framework for option discovery in reinforcement learning"
    - "the option critic architecture"

Connected through hyperedge HE-2 (induced by source paper "on reinforcement learning for turn based zero sum markov games"):
  with members:
    - "solving discounted stochastic two player games with near optimal time and sample complexity"
    - "feature based q learning for two player stochastic games"
    - "harnessing structures for value based planning and reinforcement learning"
    - "online reinforcement learning in stochastic games"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "proximal policy optimization algorithms"
    - "trust region policy optimization"
    - "value function approximation in zero sum markov games"

Connected through hyperedge HE-3 (induced by source paper "q learning for mean field controls"):
  with members:
    - "multi agent reinforcement learning a selective overview of theories and algorithms"
    - "learning to control in metric space with optimal regret"
    - "natural actor critic converges globally for hierarchical linear quadratic regulator"
    - "model based reinforcement learning for atari"
    - "an algorithmic perspective on imitation learning"
    - "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-4 (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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "recurrent value functions"):
  with members:
    - "optimizing agent behavior over long time scales by transporting value"
    - "a dissection of overfitting and generalization in continuous reinforcement learning"
    - "reward estimation for variance reduction in deep reinforcement learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "starcraft ii a new challenge for reinforcement learning"
    - "proximal policy optimization algorithms"
    - "learning values across many orders of magnitude"
    - "taming the noise in reinforcement learning via soft updates"

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)