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: "exploring model based planning with policy networks"
  Abstract: Model-based reinforcement learning (MBRL) with model-predictive control or online planning has shown great potential for locomotion control tasks in terms of both sample efficiency and asymptotic performance. Despite their initial successes, the existing planning methods search from candidate sequences randomly generated in the action space, which is inefficient in complex high-dimensional environments. In this paper, we propose a novel MBRL algorithm, model-based policy planning (POPLIN), that combines policy networks with online planning. More specifically, we formulate action planning at each time-step as an optimization problem using neural networks. We experiment with both optimization w.r.t. the action sequences initialized from the policy network, and also online optimization directly w.r.t. the parameters of the policy network. We show that POPLIN obtains state-of-the-art performance in the MuJoCo benchmarking environments, being about 3x more sample efficient than the state-of-the-art algorithms, such as PETS, TD3 and SAC. To explain the effectiveness of our algorithm, we show that the optimization surface in parameter space is smoother than in action space. Further more, we found the distilled policy network can be effectively applied without the expansive model predictive control during test time for some environments such as Cheetah. Code is released in this https URL.

Connected through hyperedge HE-1 (induced by source paper "objective mismatch in model based reinforcement learning"):
  with members:
    - "deepmdp learning continuous latent space models for representation learning"
    - "when to trust your model model based policy optimization"
    - "robust reinforcement learning for continuous control with model misspecification"
    - "universal planning networks"
    - "learning latent dynamics for planning from pixels"
    - "learning actionable representations with goal conditioned policies"
    - "differentiable mpc for end to end planning and control"
    - "solar deep structured latent representations for model based reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "the differentiable cross entropy method"):
  with members:
    - "variational inference mpc for bayesian model based reinforcement learning"
    - "the limited multi label projection layer"
    - "bayesian optimization in variational latent spaces with dynamic compression"
    - "unsupervised state representation learning in atari"
    - "adaptive and safe bayesian optimization in high dimensions via one dimensional subspaces"
    - "learning latent plans from play"
    - "learning action representations for reinforcement learning"
    - "deepmdp learning continuous latent space models for representation learning"

Connected through hyperedge HE-3 (induced by source paper "dream to control learning behaviors by latent imagination"):
  with members:
    - "imagined value gradients model based policy optimization with transferable latent dynamics models"
    - "mastering atari go chess and shogi by planning with a learned model"
    - "pipps flexible model based policy search robust to the curse of chaos"
    - "stochastic latent actor critic deep reinforcement learning with a latent variable model"
    - "model predictive policy learning with uncertainty regularization for driving in dense traffic"
    - "model based reinforcement learning for atari"
    - "deepmdp learning continuous latent space models for representation learning"
    - "on variational bounds of mutual information"

Connected through hyperedge HE-4 (induced by source paper "bootstrapping the expressivity with model based planning"):
  with members:
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "model based reinforcement learning for atari"
    - "fixup initialization residual learning without normalization"
    - "calibrated model based deep reinforcement learning"
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"
    - "model ensemble trust region policy optimization"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"

Connected through hyperedge HE-5 (induced by source paper "adaptive online planning for continual lifelong learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "when to trust your model model based policy optimization"
    - "model ensemble trust region policy optimization"
    - "off policy deep reinforcement learning without exploration"
    - "learning to search with mctsnets"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "plan online learn offline efficient learning and exploration via model based control"

Connected through hyperedge HE-6 (induced by source paper "deep dynamics models for learning dexterous manipulation"):
  with members:
    - "when to trust your model model based policy optimization"
    - "calibrated model based deep reinforcement learning"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "model ensemble trust region policy optimization"
    - "learning dexterous in hand manipulation"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "plan online learn offline efficient learning and exploration via model based control"
    - "in hand manipulation via motion cones"

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)