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: "lipschitz continuity in model based reinforcement learning"
  Abstract: Model-based reinforcement-learning methods learn transition and reward models and use them to guide behavior. We analyze the impact of learning models that are Lipschitz continuous---the distance between function values for two inputs is bounded by a linear function of the distance between the inputs. Our first result shows a tight bound on model errors for multi-step predictions with Lipschitz continuous models. We go on to prove an error bound for the value-function estimate arising from such models and show that the estimated value function is itself Lipschitz continuous. We conclude with empirical results that demonstrate significant benefits to enforcing Lipschitz continuity of neural net models during reinforcement learning.

Connected through hyperedge HE-1 (induced by source paper "deep rbf value functions for continuous control"):
  with members:
    - "caql continuous action q learning"
    - "the problem with ddpg understanding failures in deterministic environments with sparse rewards"
    - "meta gradient reinforcement learning"
    - "revisiting the softmax bellman operator new benefits and new perspective"
    - "addressing function approximation error in actor critic methods"
    - "actor expert a framework for using action value methods in continuous action spaces"
    - "rainbow combining improvements in deep reinforcement learning"
    - "deep reinforcement learning that matters"

Connected through hyperedge HE-2 (induced by source paper "neural lyapunov model predictive control"):
  with members:
    - "deep value model predictive control"
    - "differentiable convex optimization layers"
    - "tustin neural networks a class of recurrent nets for adaptive mpc of mechanical systems"
    - "accelerating neural odes with spectral elements"
    - "when to trust your model model based policy optimization"
    - "pytorch an imperative style high performance deep learning library"
    - "differentiable mpc for end to end planning and control"
    - "plan online learn offline efficient learning and exploration via model based control"

Connected through hyperedge HE-3 (induced by source paper "zooming for efficient model free reinforcement learning in metric spaces"):
  with members:
    - "learning to control in metric space with optimal regret"
    - "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 "observational overfitting in reinforcement learning"):
  with members:
    - "generalization in deep networks the role of distance from initialization"
    - "generalization in reinforcement learning with selective noise injection and information bottleneck"
    - "on the generalization gap in reparameterizable reinforcement learning"
    - "weight agnostic neural networks"
    - "implicit regularization in deep matrix factorization"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"

Connected through hyperedge HE-5 (induced by source paper "learning to predict without looking ahead world models without forward prediction"):
  with members:
    - "weight agnostic neural networks"
    - "reinforcement learning for improving agent design"
    - "model based reinforcement learning for atari"
    - "videoflow a flow based generative model for video"
    - "deep neuroevolution of recurrent and discrete world models"
    - "learning latent dynamics for planning from pixels"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "woulda coulda shoulda counterfactually guided policy search"

Connected through hyperedge HE-6 (induced by source paper "deepmdp learning continuous latent space models for representation learning"):
  with members:
    - "a comparative analysis of expected and distributional reinforcement learning"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "a geometric perspective on optimal representations for reinforcement learning"
    - "model based reinforcement learning for atari"
    - "the value function polytope in reinforcement learning"
    - "dopamine a research framework for deep reinforcement learning"
    - "combined reinforcement learning via abstract representations"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-7 (induced by source paper "exploration via hindsight goal generation"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "skew fit state covering self supervised reinforcement learning"
    - "maximum entropy regularized multi goal reinforcement learning"
    - "addressing sample complexity in visual tasks using her and hallucinatory gans"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "learning actionable representations with goal conditioned policies"
    - "visual reinforcement learning with imagined goals"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"

Connected through hyperedge HE-8 (induced by source paper "an investigation of model free planning"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "relational deep reinforcement learning"
    - "learning to search with mctsnets"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "a study on overfitting in deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"
    - "distributed prioritized experience replay"
    - "learning and querying fast generative models for 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)