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: "learning the reward function for a misspecified model"
  Abstract: In model-based reinforcement learning it is typical to treat the problems of learning the dynamics model and learning the reward function separately. However, when the dynamics model is flawed, it may generate erroneous states that would never occur in the true environment. A reward function trained only to map environment states to rewards (as is typical) would have little guidance in such states. This paper presents a novel error bound that accounts for the reward model's behavior in states sampled from the model. This bound is used to extend the existing Hallucinated DAgger-MC algorithm, which offers theoretical performance guarantees in deterministic MDPs that do not assume a perfect model can be learned. Empirically, this approach to reward learning can yield dramatic improvements in control performance when the dynamics model is flawed.

Connected through hyperedge HE-1 (induced by source paper "successor features combine elements of model free and model based reinforcement learning"):
  with members:
    - "deepmdp learning continuous latent space models for representation learning"
    - "combined reinforcement learning via abstract representations"
    - "lipschitz continuity in model based reinforcement learning"
    - "is q learning provably efficient"
    - "value prediction network"
    - "near optimal behavior via approximate state abstraction"
    - "advantages and limitations of using successor features for transfer in reinforcement learning"
    - "imagination augmented agents for deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "reward estimation for variance reduction in deep reinforcement learning"):
  with members:
    - "variational inverse control with events a general framework for data driven reward definition"
    - "combined reinforcement learning via abstract representations"
    - "model based value estimation for efficient model free reinforcement learning"
    - "deep reinforcement learning that matters"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "a distributional perspective on reinforcement learning"
    - "model based planning with discrete and continuous actions"
    - "reinforcement learning with a corrupted reward channel"

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)