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: "model based rl in contextual decision processes pac bounds and exponential improvements over model free approaches"
  Abstract: We study the sample complexity of model-based reinforcement learning (henceforth RL) in general contextual decision processes that require strategic exploration to find a near-optimal policy. We design new algorithms for RL with a generic model class and analyze their statistical properties. Our algorithms have sample complexity governed by a new structural parameter called the witness rank, which we show to be small in several settings of interest, including factored MDPs. We also show that the witness rank is never larger than the recently proposed Bellman rank parameter governing the sample complexity of the model-free algorithm OLIVE (Jiang et al., 2017), the only other provably sample-efficient algorithm for global exploration at this level of generality. Focusing on the special case of factored MDPs, we prove an exponential lower bound for a general class of model-free approaches, including OLIVE, which, when combined with our algorithmic results, demonstrates exponential separation between model-based and model-free RL in some rich-observation settings.

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 "learning near optimal policies with low inherent bellman error"):
  with members:
    - "provably efficient reinforcement learning with linear function approximation"
    - "on the optimality of sparse model based planning for markov decision processes"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "frequentist regret bounds for randomized least squares value iteration"
    - "non asymptotic gap dependent regret bounds for tabular mdps"
    - "worst case regret bounds for exploration via randomized value functions"
    - "comments on the du kakade wang yang lower bounds"
    - "tight regret bounds for model based reinforcement learning with greedy policies"

Connected through hyperedge HE-3 (induced by source paper "transfer reinforcement learning under unobserved contextual information"):
  with members:
    - "q learning with ucb exploration is sample efficient for infinite horizon mdp"
    - "importance weighted transfer of samples in reinforcement learning"
    - "least squares temporal difference learning for the linear quadratic regulator"
    - "successor features for transfer in reinforcement learning"
    - "deep recurrent q learning for partially observable mdps"
    - "transfer from multiple mdps"

Connected through hyperedge HE-4 (induced by source paper "provably efficient imitation learning from observation alone"):
  with members:
    - "information theoretic considerations in batch reinforcement learning"
    - "imitating latent policies from observation"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "behavioral cloning from observation"
    - "combining self supervised learning and imitation for vision based rope manipulation"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "agile autonomous driving using end to end deep imitation learning"

Connected through hyperedge HE-5 (induced by source paper "combating the compounding error problem with a multi step model"):
  with members:
    - "deepmdp learning continuous latent space models for representation learning"
    - "model ensemble trust region policy optimization"
    - "model based active exploration"
    - "lipschitz continuity in model based reinforcement learning"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"
    - "equivalence between wasserstein and value aware model based reinforcement learning"
    - "multi step reinforcement learning a unifying algorithm"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"

Connected through hyperedge HE-6 (induced by source paper "bootstrapping the expressivity with model based planning"):
  with members:
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "exploring model based planning with policy networks"
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "model based reinforcement learning for atari"
    - "fixup initialization residual learning without normalization"
    - "calibrated model based deep reinforcement learning"
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"
    - "model ensemble trust region policy optimization"

Connected through hyperedge HE-7 (induced by source paper "is a good representation sufficient for sample efficient reinforcement learning"):
  with members:
    - "provably efficient rl with rich observations via latent state decoding"
    - "provably efficient reinforcement learning with linear function approximation"
    - "sample optimal parametric q learning using linearly additive features"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "non asymptotic gap dependent regret bounds for tabular mdps"
    - "provably efficient q learning with function approximation via distribution shift error checking oracle"
    - "information theoretic considerations in batch reinforcement learning"
    - "a theory of regularized markov decision processes"

Connected through hyperedge HE-8 (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"
    - "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"

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)