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 "combating the compounding error problem with a multi step model"):
  Abstract: Model-based reinforcement learning is an appealing framework for creating agents that learn, plan, and act in sequential environments. Model-based algorithms typically involve learning a transition model that takes a state and an action and outputs the next state---a one-step model. This model can be composed with itself to enable predicting multiple steps into the future, but one-step prediction errors can get magnified, leading to unacceptable inaccuracy. This compounding-error problem plagues planning and undermines model-based reinforcement learning. In this paper, we address the compounding-error problem by introducing a multi-step model that directly outputs the outcome of executing a sequence of actions. Novel theoretical and empirical results indicate that the multi-step model is more conducive to efficient value-function estimation, and it yields better action selection compared to the one-step model. These results make a strong case for using multi-step models in the context of model-based reinforcement learning.

Includes members:
  Member "deepmdp learning continuous latent space models for representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neuroevolution of self interpretable agents")
  Member "model ensemble trust region policy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ready policy one world building through active learning")
  Member "model based active exploration":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "action for better prediction")
  Member "lipschitz continuity in model based reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep rbf value functions for continuous control")
  Member "algorithmic framework for model based deep reinforcement learning with theoretical guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "policy aware model learning for policy gradient methods")
  Member "equivalence between wasserstein and value aware model based reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "policy aware model learning for policy gradient methods")
  Member "model based rl in contextual decision processes pac bounds and exponential improvements over model free approaches":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "provably efficient exploration for rl with unsupervised learning")
  Member "multi step reinforcement learning a unifying algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "smix lambda enhancing centralized value functions for cooperative multi agent 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)