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 "deep model based reinforcement learning via estimated uncertainty and conservative policy optimization"):
  Abstract: Model-based reinforcement learning algorithms tend to achieve higher sample efficiency than model-free methods. However, due to the inevitable errors of learned models, model-based methods struggle to achieve the same asymptotic performance as model-free methods. #R##N#In this paper, We propose a Policy Optimization method with Model-Based Uncertainty (POMBU)---a novel model-based approach---that can effectively improve the asymptotic performance using the uncertainty in Q-values. We derive an upper bound of the uncertainty, based on which we can approximate the uncertainty accurately and efficiently for model-based methods. We further propose an uncertainty-aware policy optimization algorithm that optimizes the policy conservatively to encourage performance improvement with high probability. This can significantly alleviate the overfitting of policy to inaccurate models. #R##N#Experiments show POMBU can outperform existing state-of-the-art policy optimization algorithms in terms of sample efficiency and asymptotic performance. Moreover, the experiments demonstrate the excellent robustness of POMBU compared to previous model-based approaches.

Includes members:
  Member "model ensemble trust region policy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ready policy one world building through active learning")
  Member "sample efficient reinforcement learning with stochastic ensemble value expansion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "can increasing input dimensionality improve deep reinforcement learning")
  Member "algorithmic framework for model based deep reinforcement learning with theoretical guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "policy aware model learning for policy gradient methods")
  Member "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to walk in the real world with minimal human effort")
  Member "deep reinforcement learning in a handful of trials using probabilistic dynamics models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "the uncertainty bellman equation and exploration":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ride rewarding impact driven exploration for procedurally generated environments")
  Member "count based exploration with neural density models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "neural network dynamics for model based deep reinforcement learning with model free fine tuning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")

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)