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: "learning awareness models"
  Abstract: We consider the setting of an agent with a fixed body interacting with an unknown and uncertain external world. We show that models trained to predict proprioceptive information about the agent's body come to represent objects in the external world. In spite of being trained with only internally available signals, these dynamic body models come to represent external objects through the necessity of predicting their effects on the agent's own body. That is, the model learns holistic persistent representations of objects in the world, even though the only training signals are body signals. Our dynamics model is able to successfully predict distributions over 132 sensor readings over 100 steps into the future and we demonstrate that even when the body is no longer in contact with an object, the latent variables of the dynamics model continue to represent its shape. We show that active data collection by maximizing the entropy of predictions about the body---touch sensors, proprioception and vestibular information---leads to learning of dynamic models that show superior performance when used for control. We also collect data from a real robotic hand and show that the same models can be used to answer questions about properties of objects in the real world. Videos with qualitative results of our models are available at this https URL

Connected through hyperedge HE-1 (induced by source paper "shaping belief states with generative environment models for rl"):
  with members:
    - "conditional adversarial generative flow for controllable image synthesis"
    - "preventing posterior collapse with delta vaes"
    - "learning latent dynamics for planning from pixels"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "optimizing agent behavior over long time scales by transporting value"
    - "learning attractor dynamics for generative memory"
    - "geometric consistency for self supervised end to end visual odometry"
    - "learning and querying fast generative models for reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "causally correct partial models for reinforcement learning"):
  with members:
    - "causal discovery with reinforcement learning"
    - "mastering atari go chess and shogi by planning with a learned model"
    - "combating the compounding error problem with a multi step model"
    - "model based reinforcement learning for atari"
    - "an investigation of model free planning"
    - "when to trust your model model based policy optimization"
    - "shaping belief states with generative environment models for rl"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-3 (induced by source paper "world discovery models"):
  with members:
    - "neural predictive belief representations"
    - "learning to play with intrinsically motivated self aware agents"
    - "large scale study of curiosity driven learning"
    - "world models"
    - "observe and look further achieving consistent performance on atari"
    - "model based active exploration"
    - "representation learning with contrastive predictive coding"
    - "rainbow combining improvements in deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "zero shot learning on simulated robots"):
  with members:
    - "model based reinforcement learning for atari"
    - "world models"
    - "policy optimization with model based explorations"
    - "recurrent environment simulators"
    - "emergence of locomotion behaviours in rich environments"
    - "imagination augmented agents for deep reinforcement learning"
    - "proximal policy optimization algorithms"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-5 (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-6 (induced by source paper "graph networks as learnable physics engines for inference and control"):
  with members:
    - "deepmind control suite"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "learning a physical long term predictor"
    - "a simple neural network module for relational reasoning"
    - "learning model based planning from scratch"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "neural message passing for quantum chemistry"
    - "metacontrol for adaptive imagination based optimization"

Connected through hyperedge HE-7 (induced by source paper "temporal difference variational auto encoder"):
  with members:
    - "stochastic adversarial video prediction"
    - "learning and querying fast generative models for reinforcement learning"
    - "deep variational reinforcement learning for pomdps"
    - "adaptive skip intervals temporal abstraction for recurrent dynamical models"
    - "time agnostic prediction predicting predictable video frames"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "a distributional perspective on reinforcement learning"
    - "recurrent environment simulators"

Connected through hyperedge HE-8 (induced by source paper "neural predictive belief representations"):
  with members:
    - "integrating algorithmic planning and deep learning for partially observable navigation"
    - "unsupervised predictive memory in a goal directed agent"
    - "representation learning with contrastive predictive coding"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "learning to act by predicting the future"
    - "recurrent reinforcement learning a hybrid approach"
    - "deep residual learning for image recognition"
    - "bayesian reinforcement learning a survey"

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)