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: "recurrent environment simulators"
  Abstract: Models that can simulate how environments change in response to actions can be used by agents to plan and act efficiently. We improve on previous environment simulators from high-dimensional pixel observations by introducing recurrent neural networks that are able to make temporally and spatially coherent predictions for hundreds of time-steps into the future. We present an in-depth analysis of the factors affecting performance, providing the most extensive attempt to advance the understanding of the properties of these models. We address the issue of computationally inefficiency with a model that does not need to generate a high-dimensional image at each time-step. We show that our approach can be used to improve exploration and is adaptable to many diverse environments, namely 10 Atari games, a 3D car racing environment, and complex 3D mazes.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for autonomous driving a survey"):
  with members:
    - "yes we gan applying adversarial techniques for autonomous driving"
    - "multi objective multi agent decision making a utility based analysis and survey"
    - "model free deep reinforcement learning for urban autonomous driving"
    - "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps"
    - "soilingnet soiling detection on automotive surround view cameras"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "rlpyt a research code base for deep reinforcement learning in pytorch"
    - "on first order meta learning algorithms"

Connected through hyperedge HE-2 (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-3 (induced by source paper "learning predictive models from observation and interaction"):
  with members:
    - "experience embedded visual foresight"
    - "imitation learning from video by leveraging proprioception"
    - "improved conditional vrnns for video prediction"
    - "learning robotic manipulation through visual planning and acting"
    - "model based reinforcement learning for atari"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "adversarial imitation learning from incomplete demonstrations"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"

Connected through hyperedge HE-4 (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-5 (induced by source paper "planning with goal conditioned policies"):
  with members:
    - "skew fit state covering self supervised reinforcement learning"
    - "diagnosing and enhancing vae models"
    - "model based reinforcement learning for atari"
    - "an investigation of model free planning"
    - "temporal difference models model free deep rl for model based control"
    - "learning latent dynamics for planning from pixels"
    - "learning actionable representations with goal conditioned policies"
    - "visual reinforcement learning with imagined goals"

Connected through hyperedge HE-6 (induced by source paper "object oriented dynamics learning through multi level abstraction"):
  with members:
    - "object oriented dynamics predictor"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "relational deep reinforcement learning"
    - "deep learning for generic object detection a survey"
    - "relational inductive biases deep learning and graph networks"
    - "gotta learn fast a new benchmark for generalization in rl"
    - "contingency aware exploration in reinforcement learning"
    - "visual interaction networks"

Connected through hyperedge HE-7 (induced by source paper "making sense of sensory input"):
  with members:
    - "unsupervised grounding of plannable first order logic representation from images"
    - "model based reinforcement learning for atari"
    - "unsupervised discovery of parts structure and dynamics"
    - "composable planning with attributes"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "graph networks as learnable physics engines for inference and control"
    - "semi supervised online structure learning for composite event recognition"
    - "flexible neural representation for physics prediction"

Connected through hyperedge HE-8 (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"

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)