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: "synthesizing neural network controllers with probabilistic model based reinforcement learning"
  Abstract: We present an algorithm for rapidly learning controllers for robotics systems. The algorithm follows the model-based reinforcement learning paradigm, and improves upon existing algorithms; namely Probabilistic learning in Control (PILCO) and a sample-based version of PILCO with neural network dynamics (Deep-PILCO). We propose training a neural network dynamics model using variational dropout with truncated Log-Normal noise. This allows us to obtain a dynamics model with calibrated uncertainty, which can be used to simulate controller executions via rollouts. We also describe set of techniques, inspired by viewing PILCO as a recurrent neural network model, that are crucial to improve the convergence of the method. We test our method on a variety of benchmark tasks, demonstrating data-efficiency that is competitive with PILCO, while being able to optimize complex neural network controllers. Finally, we assess the performance of the algorithm for learning motor controllers for a six legged autonomous underwater vehicle. This demonstrates the potential of the algorithm for scaling up the dimensionality and dataset sizes, in more complex control tasks.

Connected through hyperedge HE-1 (induced by source paper "self supervised object level deep reinforcement learning"):
  with members:
    - "space unsupervised object oriented scene representation via spatial attention and decomposition"
    - "entity abstraction in visual model based reinforcement learning"
    - "unsupervised state representation learning in atari"
    - "model based reinforcement learning for atari"
    - "multi object representation learning with iterative variational inference"
    - "feature based aggregation and deep reinforcement learning a survey and some new implementations"
    - "object sensitive deep reinforcement learning"
    - "unsupervised video object segmentation for deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "uncertainty aware learning from demonstrations in multiple contexts using bayesian neural networks"):
  with members:
    - "neural decomposition of time series data for effective generalization"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "one shot visual imitation learning via meta learning"
    - "an overview of multi task learning in deep neural networks"
    - "proximal policy optimization algorithms"
    - "learning to reinforcement learn"
    - "understanding deep learning requires rethinking generalization"
    - "language modeling with gated convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "calibrated model based deep reinforcement learning"):
  with members:
    - "model ensemble trust region policy optimization"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "model based reinforcement learning via meta policy optimization"
    - "on calibration of modern neural networks"
    - "densely connected convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "a selected review on reinforcement learning based control for autonomous underwater vehicles"):
  with members:
    - "multi pseudo q learning based deterministic policy gradient for tracking control of autonomous underwater vehicles"
    - "addressing function approximation error in actor critic methods"
    - "model based value estimation for efficient model free reinforcement learning"
    - "thinking fast and slow with deep learning and tree search"
    - "proximal policy optimization algorithms"
    - "prioritized experience replay"

Connected through hyperedge HE-5 (induced by source paper "a survey on policy search algorithms for learning robot controllers in a handful of trials"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "reset free trial and error learning for robot damage recovery"
    - "an algorithmic perspective on imitation learning"
    - "composable deep reinforcement learning for robotic manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "world models"
    - "sim to real learning agile locomotion for quadruped robots"
    - "state representation learning for control an overview"

Connected through hyperedge HE-6 (induced by source paper "learning latent dynamics for planning from pixels"):
  with members:
    - "universal planning networks"
    - "deepmind control suite"
    - "model ensemble trust region policy optimization"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "stochastic video generation with a learned prior"
    - "learning and querying fast generative models for reinforcement learning"
    - "unsupervised predictive memory in a goal directed agent"

Connected through hyperedge HE-7 (induced by source paper "off policy deep reinforcement learning without exploration"):
  with members:
    - "reinforcement learning from imperfect demonstrations"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "residual policy learning"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "randomized value functions via multiplicative normalizing flows"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "representation balancing mdps for off policy policy evaluation"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"

Connected through hyperedge HE-8 (induced by source paper "deep reinforcement learning in a handful of trials using probabilistic dynamics models"):
  with members:
    - "pipps flexible model based policy search robust to the curse of chaos"
    - "model ensemble trust region policy optimization"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "on calibration of modern neural networks"
    - "combining model based and model free updates for trajectory centric reinforcement learning"
    - "searching for activation functions"
    - "proximal policy optimization algorithms"
    - "deep exploration via bootstrapped dqn"

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.RO(Robotics)