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: "benchmarking model based reinforcement learning"
  Abstract: Model-based reinforcement learning (MBRL) is widely seen as having the potential to be significantly more sample efficient than model-free RL. However, research in model-based RL has not been very standardized. It is fairly common for authors to experiment with self-designed environments, and there are several separate lines of research, which are sometimes closed-sourced or not reproducible. Accordingly, it is an open question how these various existing MBRL algorithms perform relative to each other. To facilitate research in MBRL, in this paper we gather a wide collection of MBRL algorithms and propose over 18 benchmarking environments specially designed for MBRL. We benchmark these algorithms with unified problem settings, including noisy environments. Beyond cataloguing performance, we explore and unify the underlying algorithmic differences across MBRL algorithms. We characterize three key research challenges for future MBRL research: the dynamics bottleneck, the planning horizon dilemma, and the early-termination dilemma. Finally, to maximally facilitate future research on MBRL, we open-source our benchmark in this http URL.

Connected through hyperedge HE-1 (induced by source paper "ready policy one world building through active learning"):
  with members:
    - "deep dynamics models for learning dexterous manipulation"
    - "learning to predict without looking ahead world models without forward prediction"
    - "self supervised exploration via disagreement"
    - "mastering atari go chess and shogi by planning with a learned model"
    - "demystifying active inference"
    - "model based reinforcement learning for atari"
    - "dream to control learning behaviors by latent imagination"
    - "active inference demystified and compared"

Connected through hyperedge HE-2 (induced by source paper "striving for simplicity and performance in off policy drl output normalization and non uniform sampling"):
  with members:
    - "diagnosing bottlenecks in deep q learning algorithms"
    - "remember and forget for experience replay"
    - "supervised policy update for deep reinforcement learning"
    - "clipped action policy gradient"
    - "marginal policy gradients a unified family of estimators for bounded action spaces with applications"
    - "curiosity driven experience prioritization via density estimation"
    - "safe exploration in continuous action spaces"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-3 (induced by source paper "learning functionally decomposed hierarchies for continuous control tasks"):
  with members:
    - "planning with goal conditioned policies"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "universal planning networks"
    - "deepmind control suite"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "value propagation networks"
    - "learning multi level hierarchies with hindsight"
    - "hindsight experience replay"

Connected through hyperedge HE-4 (induced by source paper "generalized hidden parameter mdps transferable model based rl in a handful of trials"):
  with members:
    - "synthesized policies for transfer and adaptation across tasks and environments"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "deepmdp learning continuous latent space models for representation learning"
    - "learning latent dynamics for planning from pixels"
    - "efficient transfer learning and online adaptation with latent variable models for continuous control"
    - "decoupling dynamics and reward for transfer learning"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "deep online learning via meta learning continual adaptation for model based rl"

Connected through hyperedge HE-5 (induced by source paper "local policy optimization for trajectory centric reinforcement learning"):
  with members:
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "guided policy search as approximate mirror descent"
    - "path integral guided policy search"
    - "end to end training of deep visuomotor policies"
    - "control design along trajectories with sums of squares programming"

Connected through hyperedge HE-6 (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"
    - "exploring model based planning with policy networks"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "model based reinforcement learning for atari"):
  with members:
    - "learning powerful policies by using consistent dynamics model"
    - "when to use parametric models in reinforcement learning"
    - "dopamine a research framework for deep reinforcement learning"
    - "learning real world robot policies by dreaming"
    - "learning latent dynamics for planning from pixels"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "model based regularization for deep reinforcement learning with transcoder networks"
    - "model ensemble trust region policy optimization"

Connected through hyperedge HE-8 (induced by source paper "combining q learning and search with amortized value estimates"):
  with members:
    - "action guidance with mcts for deep reinforcement learning"
    - "model based reinforcement learning for atari"
    - "policy gradient search online planning and expert iteration without search trees"
    - "an investigation of model free planning"
    - "unsupervised visuomotor control through distributional planning networks"
    - "structured agents for physical construction"
    - "universal planning networks"
    - "model ensemble trust region policy optimization"

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)