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: "pipps flexible model based policy search robust to the curse of chaos"
  Abstract: Previously, the exploding gradient problem has been explained to be central in deep learning and model-based reinforcement learning, because it causes numerical issues and instability in optimization. Our experiments in model-based reinforcement learning imply that the problem is not just a numerical issue, but it may be caused by a fundamental chaos-like nature of long chains of nonlinear computations. Not only do the magnitudes of the gradients become large, the direction of the gradients becomes essentially random. We show that reparameterization gradients suffer from the problem, while likelihood ratio gradients are robust. Using our insights, we develop a model-based policy search framework, Probabilistic Inference for Particle-Based Policy Search (PIPPS), which is easily extensible, and allows for almost arbitrary models and policies, while simultaneously matching the performance of previous data-efficient learning algorithms. Finally, we invent the total propagation algorithm, which efficiently computes a union over all pathwise derivative depths during a single backwards pass, automatically giving greater weight to estimators with lower variance, sometimes improving over reparameterization gradients by $10^6$ times.

Connected through hyperedge HE-1 (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"
    - "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"
    - "on variational bounds of mutual information"

Connected through hyperedge HE-2 (induced by source paper "a unified view of likelihood ratio and reparameterization gradients and an optimal importance sampling scheme"):
  with members:
    - "total stochastic gradient algorithms and applications in reinforcement learning"
    - "credit assignment techniques in stochastic computation graphs"
    - "leveraging exploration in off policy algorithms via normalizing flows"
    - "new tricks for estimating gradients of expectations"
    - "the mirage of action dependent baselines in reinforcement learning"
    - "clipped action policy gradient"
    - "understanding and correcting pathologies in the training of learned optimizers"
    - "implicit reparameterization gradients"

Connected through hyperedge HE-3 (induced by source paper "regularizing trajectory optimization with denoising autoencoders"):
  with members:
    - "temporal difference models model free deep rl for model based control"
    - "model ensemble trust region policy optimization"
    - "improving model based control and active exploration with reconstruction uncertainty optimization"
    - "plan online learn offline efficient learning and exploration via model based control"
    - "addressing function approximation error in actor critic methods"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "model based reinforcement learning via meta policy optimization"
    - "threat of adversarial attacks on deep learning in computer vision a survey"

Connected through hyperedge HE-4 (induced by source paper "regularizing model based planning with energy based models"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "regularizing trajectory optimization with denoising autoencoders"
    - "model based planning with energy based models"
    - "learning latent dynamics for planning from pixels"
    - "model ensemble trust region policy optimization"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "plan online learn offline efficient learning and exploration via model based control"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-5 (induced by source paper "total stochastic gradient algorithms and applications in reinforcement learning"):
  with members:
    - "variational inference for monte carlo objectives"
    - "gradient estimation using stochastic computation graphs"
    - "learning continuous control policies by stochastic value gradients"

Connected through hyperedge HE-6 (induced by source paper "understanding and correcting pathologies in the training of learned optimizers"):
  with members:
    - "understanding short horizon bias in stochastic meta optimization"
    - "learning to optimize neural nets"
    - "aggregated momentum stability through passive damping"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "unbiasing truncated backpropagation through time"
    - "evolved policy gradients"
    - "learning gradient descent better generalization and longer horizons"
    - "learned optimizers that scale and generalize"

Connected through hyperedge HE-7 (induced by source paper "deep reinforcement learning in a handful of trials using probabilistic dynamics models"):
  with members:
    - "model ensemble trust region policy optimization"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "synthesizing neural network controllers with probabilistic model based reinforcement learning"
    - "on calibration of modern neural networks"
    - "combining model based and model free updates for trajectory centric reinforcement learning"
    - "searching for activation functions"
    - "proximal policy optimization algorithms"
    - "deep exploration via bootstrapped dqn"

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)