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: "model based reinforcement learning for atari"
  Abstract: Model-free reinforcement learning (RL) can be used to learn effective policies for complex tasks, such as Atari games, even from image observations. However, this typically requires very large amounts of interaction -- substantially more, in fact, than a human would need to learn the same games. How can people learn so quickly? Part of the answer may be that people can learn how the game works and predict which actions will lead to desirable outcomes. In this paper, we explore how video prediction models can similarly enable agents to solve Atari games with fewer interactions than model-free methods. We describe Simulated Policy Learning (SimPLe), a complete model-based deep RL algorithm based on video prediction models and present a comparison of several model architectures, including a novel architecture that yields the best results in our setting. Our experiments evaluate SimPLe on a range of Atari games in low data regime of $100$K interactions between the agent and the environment, which corresponds to two hours of real-time play. In most games SimPLe outperforms state-of-the-art model-free algorithms, in some games by over an order of magnitude.

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"
    - "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"
    - "world models"

Connected through hyperedge HE-2 (induced by source paper "neuroevolution of self interpretable agents"):
  with members:
    - "rotation translation and cropping for zero shot generalization"
    - "skin lesion classification using cnns with patch based attention and diagnosis guided loss weighting"
    - "improving deep neuroevolution via deep innovation protection"
    - "investigating generalisation in continuous deep reinforcement learning"
    - "leveraging procedural generation to benchmark reinforcement learning"
    - "emergent systematic generalization in a situated agent"
    - "continual learning with hypernetworks"
    - "learning to generalize from sparse and underspecified rewards"

Connected through hyperedge HE-3 (induced by source paper "action for better prediction"):
  with members:
    - "self supervised exploration via disagreement"
    - "curious ilqr resolving uncertainty in model based rl"
    - "manipulation by feel touch based control with deep predictive models"
    - "unsupervised minimax adversarial curiosity generative adversarial networks and predictability minimization"
    - "robonet large scale multi robot learning"
    - "learning predictive models from observation and interaction"
    - "exploration by random network distillation"
    - "reasoning about physical interactions with object oriented prediction and planning"

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"
    - "dream to control learning behaviors by latent imagination"
    - "benchmarking model based reinforcement learning"
    - "active inference demystified and compared"

Connected through hyperedge HE-5 (induced by source paper "an optimistic perspective on offline reinforcement learning"):
  with members:
    - "keep doing what worked behavioral modelling priors for offline reinforcement learning"
    - "benchmarking batch deep reinforcement learning algorithms"
    - "off policy policy gradient with state distribution correction"
    - "behavior regularized offline reinforcement learning"
    - "when to use parametric models in reinforcement learning"
    - "challenges of real world reinforcement learning"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "a framework for data driven robotics"

Connected through hyperedge HE-6 (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"
    - "efficient exploration via state marginal matching"
    - "dream to control learning behaviors by latent imagination"
    - "bayesian policy selection using active inference"

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"
    - "bayesian variational autoencoders for unsupervised out of distribution detection"
    - "deepmind control suite"

Connected through hyperedge HE-8 (induced by source paper "active perception and representation for robotic manipulation"):
  with members:
    - "saccader improving accuracy of hard attention models for vision"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "learning actionable representations from visual observations"
    - "visual reinforcement learning with imagined goals"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "soft actor critic algorithms and applications"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"

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)