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 and querying fast generative models for reinforcement learning"
  Abstract: A key challenge in model-based reinforcement learning (RL) is to synthesize computationally efficient and accurate environment models. We show that carefully designed generative models that learn and operate on compact state representations, so-called state-space models, substantially reduce the computational costs for predicting outcomes of sequences of actions. Extensive experiments establish that state-space models accurately capture the dynamics of Atari games from the Arcade Learning Environment from raw pixels. The computational speed-up of state-space models while maintaining high accuracy makes their application in RL feasible: We demonstrate that agents which query these models for decision making outperform strong model-free baselines on the game MSPACMAN, demonstrating the potential of using learned environment models for planning.

Connected through hyperedge HE-1 (induced by source paper "relational state space model for stochastic multi object systems"):
  with members:
    - "stochastic prediction of multi agent interactions from partial observations"
    - "graph normalizing flows"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "social bigat multimodal trajectory forecasting using bicycle gan and graph attention networks"
    - "emerging convolutions for generative normalizing flows"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "state space lstm models with particle mcmc inference"

Connected through hyperedge HE-2 (induced by source paper "dream to control learning behaviors by latent imagination"):
  with members:
    - "imagined value gradients model based policy optimization with transferable latent dynamics models"
    - "exploring model based planning with policy networks"
    - "mastering atari go chess and shogi by planning with a learned model"
    - "pipps flexible model based policy search robust to the curse of chaos"
    - "stochastic latent actor critic deep reinforcement learning with a latent variable model"
    - "model predictive policy learning with uncertainty regularization for driving in dense traffic"
    - "model based reinforcement learning for atari"
    - "deepmdp learning continuous latent space models for representation learning"

Connected through hyperedge HE-3 (induced by source paper "visual reaction learning to play catch with your drone"):
  with members:
    - "relational action forecasting"
    - "high fidelity video prediction with large stochastic recurrent neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning latent dynamics for planning from pixels"
    - "model ensemble trust region policy optimization"
    - "semi parametric topological memory for navigation"
    - "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"
    - "visual semantic navigation using scene priors"

Connected through hyperedge HE-4 (induced by source paper "reinforcement learning based visual navigation with information theoretic regularization"):
  with members:
    - "learning a controller fusion network by online trajectory filtering for vision based uav racing"
    - "sim real joint reinforcement transfer for 3d indoor navigation"
    - "splitnet sim2sim and task2task transfer for embodied visual navigation"
    - "bayesian relational memory for semantic visual navigation"
    - "reinforcement learning from imperfect demonstrations"
    - "deep drone racing learning agile flight in dynamic environments"
    - "a real time game theoretic planner for autonomous two player drone racing"
    - "oil observational imitation learning"

Connected through hyperedge HE-5 (induced by source paper "stochastic prediction of multi agent interactions from partial observations"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "graph networks as learnable physics engines for inference and control"
    - "stochastic adversarial video prediction"
    - "generative multi agent behavioral cloning"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "stochastic video generation with a learned prior"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-6 (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"
    - "visual semantic navigation using scene priors"
    - "learning to navigate in cities without a map"
    - "gated path planning networks"

Connected through hyperedge HE-7 (induced by source paper "credit assignment techniques in stochastic computation graphs"):
  with members:
    - "total stochastic gradient algorithms and applications in reinforcement learning"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "the mirage of action dependent baselines in reinforcement learning"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "implicit reparameterization gradients"
    - "deep variational reinforcement learning for pomdps"

Connected through hyperedge HE-8 (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"

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)