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: "data efficient learning of feedback policies from image pixels using deep dynamical models"
  Abstract: Data-efficient reinforcement learning (RL) in continuous state-action spaces using very high-dimensional observations remains a key challenge in developing fully autonomous systems. We consider a particularly important instance of this challenge, the pixels-to-torques problem, where an RL agent learns a closed-loop control policy ("torques") from pixel information only. We introduce a data-efficient, model-based reinforcement learning algorithm that learns such a closed-loop policy directly from pixel information. The key ingredient is a deep dynamical model for learning a low-dimensional feature embedding of images jointly with a predictive model in this low-dimensional feature space. Joint learning is crucial for long-term predictions, which lie at the core of the adaptive nonlinear model predictive control strategy that we use for closed-loop control. Compared to state-of-the-art RL methods for continuous states and actions, our approach learns quickly, scales to high-dimensional state spaces, is lightweight and an important step toward fully autonomous end-to-end learning from pixels to torques.

Connected through hyperedge HE-1 (induced by source paper "state representation learning from demonstration"):
  with members:
    - "weight agnostic neural networks"
    - "s rl toolbox environments datasets and evaluation metrics for state representation learning"
    - "deepmind control suite"
    - "world models"
    - "state representation learning for control an overview"
    - "hindsight experience replay"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "one shot visual imitation learning via meta learning"

Connected through hyperedge HE-2 (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-3 (induced by source paper "solar deep structured latent representations for model based reinforcement learning"):
  with members:
    - "variational inverse control with events a general framework for data driven reward definition"
    - "learning latent dynamics for planning from pixels"
    - "learning dexterous in hand manipulation"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "state representation learning for control an overview"
    - "addressing function approximation error in actor critic methods"
    - "model based value estimation for efficient model free reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "state representation learning for control an overview"):
  with members:
    - "decoupling dynamics and reward for transfer learning"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "deep reinforcement learning that matters"
    - "progressive neural networks"
    - "independently controllable factors"
    - "deep multimodal representation learning from temporal data"
    - "interactive perception leveraging action in perception and perception in action"

Connected through hyperedge HE-5 (induced by source paper "deep reinforcement learning for dynamic multichannel access in wireless networks"):
  with members:
    - "tensorflow a system for large scale machine learning"
    - "continuous control with deep reinforcement learning"
    - "end to end training of deep visuomotor policies"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "multiple object recognition with visual attention"
    - "sequence to sequence learning with neural networks"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "generative temporal models with memory"):
  with members:
    - "one shot learning with memory augmented neural networks"
    - "text understanding with the attention sum reader network"
    - "programming with a differentiable forth interpreter"
    - "learning to generate with memory"
    - "attend infer repeat fast scene understanding with generative models"
    - "end to end memory networks"
    - "deep residual learning for image recognition"
    - "a recurrent latent variable model for sequential data"

Connected through hyperedge HE-7 (induced by source paper "design of deep neural networks as add on blocks for improving impromptu trajectory tracking"):
  with members:
    - "on the construction of safe controllable regions for affine systems with applications to robotics"
    - "safe controller optimization for quadrotors with gaussian processes"
    - "real time optimal control via deep neural networks study on landing problems"
    - "deep neural networks for improved impromptu trajectory tracking of quadrotors"

Connected through hyperedge HE-8 (induced by source paper "learning to communicate to solve riddles with deep distributed recurrent q networks"):
  with members:
    - "prioritized experience replay"
    - "recurrent reinforcement learning a hybrid approach"
    - "increasing the action gap new operators for reinforcement learning"
    - "language understanding for text based games using deep reinforcement learning"
    - "embed to control a locally linear latent dynamics model for control from raw images"
    - "action conditional video prediction using deep networks in atari games"
    - "dueling network architectures for deep reinforcement learning"
    - "deep recurrent q learning for partially observable mdps"

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.AI(Artificial Intelligence)