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: "when to trust your model model based policy optimization"
  Abstract: Designing effective model-based reinforcement learning algorithms is difficult because the ease of data generation must be weighed against the bias of model-generated data. In this paper, we study the role of model usage in policy optimization both theoretically and empirically. We first formulate and analyze a model-based reinforcement learning algorithm with a guarantee of monotonic improvement at each step. In practice, this analysis is overly pessimistic and suggests that real off-policy data is always preferable to model-generated on-policy data, but we show that an empirical estimate of model generalization can be incorporated into such analysis to justify model usage. Motivated by this analysis, we then demonstrate that a simple procedure of using short model-generated rollouts branched from real data has the benefits of more complicated model-based algorithms without the usual pitfalls. In particular, this approach surpasses the sample efficiency of prior model-based methods, matches the asymptotic performance of the best model-free algorithms, and scales to horizons that cause other model-based methods to fail entirely.

Connected through hyperedge HE-1 (induced by source paper "learning to walk in the real world with minimal human effort"):
  with members:
    - "safety guided deep reinforcement learning via online gaussian process estimation"
    - "solving rubik s cube with a robot hand"
    - "generalizing from a few environments in safety critical reinforcement learning"
    - "data efficient reinforcement learning for legged robots"
    - "deepgait planning and control of quadrupedal gaits using deep reinforcement learning"
    - "norml no reward meta learning"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "learning agile and dynamic motor skills for legged robots"

Connected through hyperedge HE-2 (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"
    - "benchmarking model based reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "objective mismatch in model based reinforcement learning"):
  with members:
    - "exploring model based planning with policy networks"
    - "deepmdp learning continuous latent space models for representation learning"
    - "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-4 (induced by source paper "neural lyapunov model predictive control"):
  with members:
    - "deep value model predictive control"
    - "differentiable convex optimization layers"
    - "tustin neural networks a class of recurrent nets for adaptive mpc of mechanical systems"
    - "accelerating neural odes with spectral elements"
    - "pytorch an imperative style high performance deep learning library"
    - "differentiable mpc for end to end planning and control"
    - "lipschitz continuity in model based reinforcement learning"
    - "plan online learn offline efficient learning and exploration via model based control"

Connected through hyperedge HE-5 (induced by source paper "placement optimization with deep reinforcement learning"):
  with members:
    - "gdp generalized device placement for dataflow graphs"
    - "a comprehensive survey on graph neural networks"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "device placement optimization with reinforcement learning"
    - "proximal policy optimization algorithms"
    - "asynchronous methods for deep reinforcement learning"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "deep convolutional networks on graph structured data"

Connected through hyperedge HE-6 (induced by source paper "efficient reinforcement learning control for continuum robots based on inexplicit prior knowledge"):
  with members:
    - "deep reinforcement learning boosted by external knowledge"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "model ensemble trust region policy optimization"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "model based value estimation for efficient model free reinforcement learning"
    - "value prediction network"

Connected through hyperedge HE-7 (induced by source paper "planning and execution using inaccurate models with provable guarantees"):
  with members:
    - "modeling cooperative navigation in dense human crowds"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "learning predictive models from observation and interaction"):
  with members:
    - "experience embedded visual foresight"
    - "imitation learning from video by leveraging proprioception"
    - "improved conditional vrnns for video prediction"
    - "learning robotic manipulation through visual planning and acting"
    - "model based reinforcement learning for atari"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "adversarial imitation learning from incomplete demonstrations"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"

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)