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: "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
  Abstract: Model-based reinforcement learning (RL) algorithms can attain excellent sample efficiency, but often lag behind the best model-free algorithms in terms of asymptotic performance. This is especially true with high-capacity parametric function approximators, such as deep networks. In this paper, we study how to bridge this gap, by employing uncertainty-aware dynamics models. We propose a new algorithm called probabilistic ensembles with trajectory sampling (PETS) that combines uncertainty-aware deep network dynamics models with sampling-based uncertainty propagation. Our comparison to state-of-the-art model-based and model-free deep RL algorithms shows that our approach matches the asymptotic performance of model-free algorithms on several challenging benchmark tasks, while requiring significantly fewer samples (e.g., 8 and 125 times fewer samples than Soft Actor Critic and Proximal Policy Optimization respectively on the half-cheetah task).

Connected through hyperedge HE-1 (induced by source paper "probabilistic future prediction for video scene understanding"):
  with members:
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "model based reinforcement learning for atari"
    - "adversarial video generation on complex datasets"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning latent dynamics for planning from pixels"
    - "learning plannable representations with causal infogan"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"

Connected through hyperedge HE-2 (induced by source paper "learning to fly via deep model based reinforcement learning"):
  with members:
    - "deep drone racing from simulation to reality with domain randomization"
    - "solving rubik s cube with a robot hand"
    - "imagined value gradients model based policy optimization with transferable latent dynamics models"
    - "flying through a narrow gap using neural network an end to end planning and control approach"
    - "teach repeat replan a complete and robust system for aggressive flight in complex environments"
    - "neuroflight next generation flight control firmware"
    - "robust and efficient quadrotor trajectory generation for fast autonomous flight"
    - "real time reinforcement learning"

Connected through hyperedge HE-3 (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-4 (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-5 (induced by source paper "reinforcement learning through active inference"):
  with members:
    - "variational inference mpc for bayesian model based reinforcement learning"
    - "deep active inference as variational policy gradients"
    - "a unified bellman optimality principle combining reward maximization and empowerment"
    - "deep dynamics models for learning dexterous manipulation"
    - "demystifying active inference"
    - "model based reinforcement learning for atari"
    - "efficient exploration via state marginal matching"
    - "dream to control learning behaviors by latent imagination"

Connected through hyperedge HE-6 (induced by source paper "domain adversarial and conditional state space model for imitation learning"):
  with members:
    - "to follow or not to follow selective imitation learning from observations"
    - "variational inference mpc for bayesian model based reinforcement learning"
    - "integration of imitation learning using gail and reinforcement learning using task achievement rewards via probabilistic generative model"
    - "learning belief representations for imitation learning in pomdps"
    - "learning robotic manipulation through visual planning and acting"
    - "stochastic latent actor critic deep reinforcement learning with a latent variable model"
    - "recent advances in imitation learning from observation"
    - "reinforcement learning and control as probabilistic inference tutorial and review"

Connected through hyperedge HE-7 (induced by source paper "planet of the bayesians reconsidering and improving deep planning network by incorporating bayesian inference"):
  with members:
    - "domain adversarial and conditional state space model for imitation learning"
    - "variational inference mpc for bayesian model based reinforcement learning"
    - "variational recurrent models for solving partially observable control tasks"
    - "deep dynamics models for learning dexterous manipulation"
    - "mastering atari go chess and shogi by planning with a learned model"
    - "stochastic latent actor critic deep reinforcement learning with a latent variable model"
    - "model based reinforcement learning for atari"
    - "bayesian variational autoencoders for unsupervised out of distribution detection"

Connected through hyperedge HE-8 (induced by source paper "automatic differentiation and continuous sensitivity analysis of rigid body dynamics"):
  with members:
    - "densephysnet learning dense physical object representations via multi step dynamic interactions"
    - "deep lagrangian networks using physics as model prior for deep learning"
    - "hamiltonian neural networks"
    - "deepmind control suite"
    - "a comparison of automatic differentiation and continuous sensitivity analysis for derivatives of differential equation solutions"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "flexible neural representation for physics prediction"

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)