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: "a deep learning approach for joint video frame and reward prediction in atari games"
  Abstract: Reinforcement learning is concerned with identifying reward-maximizing behaviour policies in environments that are initially unknown. State-of-the-art reinforcement learning approaches, such as deep Q-networks, are model-free and learn to act effectively across a wide range of environments such as Atari games, but require huge amounts of data. Model-based techniques are more data-efficient, but need to acquire explicit knowledge about the environment. #R##N#In this paper, we take a step towards using model-based techniques in environments with a high-dimensional visual state space by demonstrating that it is possible to learn system dynamics and the reward structure jointly. Our contribution is to extend a recently developed deep neural network for video frame prediction in Atari games to enable reward prediction as well. To this end, we phrase a joint optimization problem for minimizing both video frame and reward reconstruction loss, and adapt network parameters accordingly. Empirical evaluations on five Atari games demonstrate accurate cumulative reward prediction of up to 200 frames. We consider these results as opening up important directions for model-based reinforcement learning in complex, initially unknown environments.

Connected through hyperedge HE-1 (induced by source paper "disentangling controllable object through video prediction improves visual reinforcement learning"):
  with members:
    - "model based reinforcement learning for atari"
    - "learning latent dynamics for planning from pixels"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "contingency aware exploration in reinforcement learning"
    - "independently controllable features"
    - "curiosity driven exploration by self supervised prediction"
    - "unsupervised learning for physical interaction through video prediction"

Connected through hyperedge HE-2 (induced by source paper "model based reinforcement learning for atari"):
  with members:
    - "learning powerful policies by using consistent dynamics model"
    - "when to use parametric models in reinforcement learning"
    - "benchmarking model based reinforcement learning"
    - "dopamine a research framework for deep reinforcement learning"
    - "learning real world robot policies by dreaming"
    - "learning latent dynamics for planning from pixels"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "model based regularization for deep reinforcement learning with transcoder networks"

Connected through hyperedge HE-3 (induced by source paper "neonav improving the generalization of visual navigation via generating next expected observations"):
  with members:
    - "splitnet sim2sim and task2task transfer for embodied visual navigation"
    - "habitat a platform for embodied ai research"
    - "semi parametric topological memory for navigation"
    - "on evaluation of embodied navigation agents"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "learning and querying fast generative models for reinforcement learning"
    - "visual semantic navigation using scene priors"
    - "learning to navigate in cities without a map"

Connected through hyperedge HE-4 (induced by source paper "generative adversarial imagination for sample efficient deep reinforcement learning"):
  with members:
    - "model based reinforcement learning for atari"
    - "surprising negative results for generative adversarial tree search"
    - "distributed distributional deterministic policy gradients"
    - "rainbow combining improvements in deep reinforcement learning"
    - "deep reinforcement learning a brief survey"
    - "a distributional perspective on reinforcement learning"
    - "recurrent environment simulators"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-5 (induced by source paper "snooping attacks on deep reinforcement learning"):
  with members:
    - "zero shot visual imitation"
    - "adversarial attacks on neural network policies"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "robust deep reinforcement learning with adversarial attacks"
    - "vulnerability of deep reinforcement learning to policy induction attacks"
    - "visualizing and understanding atari agents"
    - "asymmetric actor critic for image based robot learning"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-6 (induced by source paper "model based reinforcement learning for predictions and control for limit order books"):
  with members:
    - "model based reinforcement learning for whole chain recommendations"
    - "model predictive policy learning with uncertainty regularization for driving in dense traffic"
    - "model based reinforcement learning for atari"
    - "world models"
    - "learning montezuma s revenge from a single demonstration"
    - "benchmark dataset for mid price forecasting of limit order book data with machine learning methods"
    - "solar deep structured latent representations for model based reinforcement learning"
    - "flow a modular learning framework for autonomy in traffic"

Connected through hyperedge HE-7 (induced by source paper "terminal prediction as an auxiliary task for deep reinforcement learning"):
  with members:
    - "a geometric perspective on optimal representations for reinforcement learning"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "back to basics benchmarking canonical evolution strategies for playing atari"
    - "backplay man muss immer umkehren"
    - "an introduction to deep reinforcement learning"
    - "deep reinforcement learning a brief survey"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "sdcnet video prediction using spatially displaced convolution"):
  with members:
    - "stochastic video generation with a learned prior"
    - "predicting deeper into the future of semantic segmentation"
    - "decomposing motion and content for natural video sequence prediction"
    - "contextvp fully context aware video prediction"
    - "folded recurrent neural networks for future video prediction"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "stochastic variational video prediction"
    - "video frame interpolation via adaptive convolution"

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)