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: "robust locally linear controllable embedding"
  Abstract: Embed-to-control (E2C) is a model for solving high-dimensional optimal control problems by combining variational auto-encoders with locally-optimal controllers. However, the E2C model suffers from two major drawbacks: 1) its objective function does not correspond to the likelihood of the data sequence and 2) the variational encoder used for embedding typically has large variational approximation error, especially when there is noise in the system dynamics. In this paper, we present a new model for learning robust locally-linear controllable embedding (RCE). Our model directly estimates the predictive conditional density of the future observation given the current one, while introducing the bottleneck between the current and future observations. Although the bottleneck provides a natural embedding candidate for control, our RCE model introduces additional specific structures in the generative graphical model so that the model dynamics can be robustly linearized. We also propose a principled variational approximation of the embedding posterior that takes the future observation into account, and thus, makes the variational approximation more robust against the noise. Experimental results show that RCE outperforms the E2C model, and does so significantly when the underlying dynamics is noisy.

Connected through hyperedge HE-1 (induced by source paper "predictive coding for locally linear control"):
  with members:
    - "predictive coding for boosting deep reinforcement learning with sparse rewards"
    - "unsupervised state representation learning in atari"
    - "model based reinforcement learning for atari"
    - "on variational bounds of mutual information"
    - "prediction consistency curvature representation learning for locally linear control"
    - "noise contrastive estimation and negative sampling for conditional models consistency and statistical efficiency"
    - "learning latent dynamics for planning from pixels"
    - "near optimal representation learning for hierarchical reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "learning when to trust a dynamics model for planning in reduced state spaces"):
  with members:
    - "manipulating deformable objects by interleaving prediction planning and control"
    - "learning robotic manipulation through visual planning and acting"
    - "robot motion planning in learned latent spaces"
    - "learning latent space dynamics for tactile servoing"
    - "manipulating highly deformable materials using a visual feedback dictionary"
    - "deep visual foresight for planning robot motion"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "the differentiable cross entropy method"):
  with members:
    - "variational inference mpc for bayesian model based reinforcement learning"
    - "exploring model based planning with policy networks"
    - "the limited multi label projection layer"
    - "bayesian optimization in variational latent spaces with dynamic compression"
    - "unsupervised state representation learning in atari"
    - "adaptive and safe bayesian optimization in high dimensions via one dimensional subspaces"
    - "learning latent plans from play"
    - "learning action representations for reinforcement learning"

Connected through hyperedge HE-4 (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-5 (induced by source paper "learning robotic manipulation through visual planning and acting"):
  with members:
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning robotic assembly from cad"
    - "model ensemble trust region policy optimization"
    - "visual reinforcement learning with imagined goals"
    - "learning plannable representations with causal infogan"
    - "spectral normalization for generative adversarial networks"
    - "bias and generalization in deep generative models an empirical study"

Connected through hyperedge HE-6 (induced by source paper "learned critical probabilistic roadmaps for robotic motion planning"):
  with members:
    - "lego leveraging experience in roadmap generation for sampling based planning"
    - "long range indoor navigation with prm rl"
    - "identifying critical regions for motion planning using auto generated saliency labels with convolutional neural networks"
    - "using local experiences for global motion planning"
    - "robot motion planning in learned latent spaces"
    - "the laplacian in rl learning representations with efficient approximations"
    - "learning implicit sampling distributions for motion planning"
    - "learning sampling distributions for robot motion planning"

Connected through hyperedge HE-7 (induced by source paper "learning latent state spaces for planning through reward prediction"):
  with members:
    - "deepmdp learning continuous latent space models for representation learning"
    - "robot motion planning in learned latent spaces"
    - "learning latent dynamics for planning from pixels"
    - "large scale study of curiosity driven learning"
    - "soft actor critic algorithms and applications"
    - "solar deep structured latent representations for model based reinforcement learning"
    - "value prediction network"
    - "deep visual foresight for planning robot motion"

Connected through hyperedge HE-8 (induced by source paper "video extrapolation with an invertible linear embedding"):
  with members:
    - "a dataset to evaluate the representations learned by video prediction models"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "recurrent environment simulators"
    - "density estimation using real nvp"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "embed to control a locally linear latent dynamics model for control from raw images"
    - "action conditional video prediction using deep networks in atari games"
    - "deep multi scale video prediction beyond mean square error"

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)