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: "pac bounds for discounted mdps"
  Abstract: We study upper and lower bounds on the sample-complexity of learning near-optimal behaviour in finite-state discounted Markov Decision Processes (MDPs). For the upper bound we make the assumption that each action leads to at most two possible next-states and prove a new bound for a UCRL-style algorithm on the number of time-steps when it is not Probably Approximately Correct (PAC). The new lower bound strengthens previous work by being both more general (it applies to all policies) and tighter. The upper and lower bounds match up to logarithmic factors.

Connected through hyperedge HE-1 (induced by source paper "agnostic q learning with function approximation in deterministic systems tight bounds on approximation error and sample complexity"):
  with members:
    - "efficient and robust algorithms for adversarial linear contextual bandits"
    - "provably efficient reinforcement learning with linear function approximation"
    - "on the optimality of sparse model based planning for markov decision processes"
    - "sample optimal parametric q learning using linearly additive features"
    - "non asymptotic gap dependent regret bounds for tabular mdps"
    - "finite sample analysis for sarsa with linear function approximation"
    - "provably efficient q learning with function approximation via distribution shift error checking oracle"
    - "comments on the du kakade wang yang lower bounds"

Connected through hyperedge HE-2 (induced by source paper "invariant causal prediction for block mdps"):
  with members:
    - "deepmdp learning continuous latent space models for representation learning"
    - "meta learning without memorization"
    - "attention privileged reinforcement learning for domain transfer"
    - "deepmind control suite"
    - "natural environment benchmarks for reinforcement learning"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"
    - "a study on overfitting in deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "regret bounds for discounted mdps"):
  with members:
    - "q learning with ucb exploration is sample efficient for infinite horizon mdp"
    - "is q learning provably efficient"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "variance reduction methods for sublinear reinforcement learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "proximal policy optimization algorithms"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "provably efficient reinforcement learning with linear function approximation"):
  with members:
    - "sample optimal parametric q learning using linearly additive features"
    - "variance reduced q learning is minimax optimal"
    - "provably efficient q learning with function approximation via distribution shift error checking oracle"
    - "learning linear quadratic regulators efficiently with only t regret"
    - "reinforcement leaning in feature space matrix bandit kernels and regret bound"
    - "lipschitz bandit optimization with improved efficiency"
    - "model free linear quadratic control via reduction to expert prediction"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"

Connected through hyperedge HE-5 (induced by source paper "kinematic state abstraction and provably efficient rich observation reinforcement learning"):
  with members:
    - "sample complexity of reinforcement learning using linearly combined model ensembles"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "information theoretic considerations in batch reinforcement learning"
    - "provably efficient maximum entropy exploration"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "ucb exploration via q ensembles"
    - "learning to reason end to end module networks for visual question answering"
    - "ucb and infogain exploration via boldsymbol q ensembles"

Connected through hyperedge HE-6 (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-7 (induced by source paper "regret minimization for reinforcement learning by evaluating the optimal bias function"):
  with members:
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "efficient bias span constrained exploration exploitation in reinforcement learning"
    - "variance reduction methods for sublinear reinforcement learning"
    - "posterior sampling for large scale reinforcement learning"
    - "learning unknown markov decision processes a thompson sampling approach"
    - "regal a regularization based algorithm for reinforcement learning in weakly communicating mdps"

Connected through hyperedge HE-8 (induced by source paper "q learning with ucb exploration is sample efficient for infinite horizon mdp"):
  with members:
    - "is q learning provably efficient"
    - "variance reduced value iteration and faster algorithms for solving markov decision processes"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "playing atari with deep reinforcement learning"

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)