human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "equivalence between wasserstein and value aware model based reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "policy aware model learning for policy gradient methods")
  Member "lipschitz properties for deep convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient and accurate estimation of lipschitz constants for deep neural networks")
  Member "a distributional perspective on reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the hanabi challenge a new frontier for ai research")
  Member "bridging the gap between value and policy based reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "a unified view of entropy regularized markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reinforcement learning via fenchel rockafellar duality")
  Member "continuous deep q learning with model based acceleration":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust deep reinforcement learning against adversarial perturbations on observations")
  Member "self correcting models for model based reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "policy aware model learning for policy gradient methods")
  Member "an alternative softmax operator for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep rbf value functions for continuous control")

Question: Please tell me which class the source paper 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)