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: "prediction and control with temporal segment models"
  Abstract: We introduce a method for learning the dynamics of complex nonlinear systems based on deep generative models over temporal segments of states and actions. Unlike dynamics models that operate over individual discrete timesteps, we learn the distribution over future state trajectories conditioned on past state, past action, and planned future action trajectories, as well as a latent prior over action trajectories. Our approach is based on convolutional autoregressive models and variational autoencoders. It makes stable and accurate predictions over long horizons for complex, stochastic systems, effectively expressing uncertainty and modeling the effects of collisions, sensory noise, and action delays. The learned dynamics model and action prior can be used for end-to-end, fully differentiable trajectory optimization and model-based policy optimization, which we use to evaluate the performance and sample-efficiency of our method.

Connected through hyperedge HE-1 (induced by source paper "sub goal trees a framework for goal based reinforcement learning"):
  with members:
    - "harnessing reinforcement learning for neural motion planning"
    - "auto conditioned recurrent mixture density networks for complex trajectory generation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "motion planning networks"
    - "floyd warshall reinforcement learning learning from past experiences to reach new goals"
    - "hindsight experience replay"
    - "proximal policy optimization algorithms"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"

Connected through hyperedge HE-2 (induced by source paper "learning stable deep dynamics models"):
  with members:
    - "episodic learning with control lyapunov functions for uncertain robotic systems"
    - "the lyapunov neural network adaptive stability certification for safe learning of dynamical systems"
    - "a lyapunov based approach to safe reinforcement learning"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "continuous deep q learning with model based acceleration"
    - "input convex neural networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-3 (induced by source paper "regularizing model based planning with energy based models"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "pipps flexible model based policy search robust to the curse of chaos"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "multi agent reinforcement learning with multi step generative models"):
  with members:
    - "learning latent dynamics for planning from pixels"
    - "model ensemble trust region policy optimization"
    - "deep imitative models for flexible inference planning and control"
    - "generative modeling of multimodal multi human behavior"
    - "unity a general platform for intelligent agents"
    - "learning latent subspaces in variational autoencoders"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "emergence of grounded compositional language in multi agent populations"

Connected through hyperedge HE-5 (induced by source paper "model based planning with energy based models"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "exponential family estimation via adversarial dynamics embedding"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "learning dexterous in hand manipulation"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "gotta learn fast a new benchmark for generalization in rl"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"

Connected through hyperedge HE-6 (induced by source paper "learning hybrid object kinematics for efficient hierarchical planning under uncertainty"):
  with members:
    - "category level articulated object pose estimation"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "unsupervised discovery of parts structure and dynamics"
    - "inferring geometric constraints in human demonstrations"
    - "efficient hierarchical robot motion planning under uncertainty and hybrid dynamics"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "se3 nets learning rigid body motion using deep neural networks"
    - "learning articulated motions from visual demonstration"

Connected through hyperedge HE-7 (induced by source paper "sub goal trees a framework for goal directed trajectory prediction and optimization"):
  with members:
    - "auto conditioned recurrent mixture density networks for complex trajectory generation"
    - "deep learning using rectified linear units relu"
    - "motion planning networks"
    - "floyd warshall reinforcement learning learning from past experiences to reach new goals"
    - "time agnostic prediction predicting predictable video frames"
    - "hindsight experience replay"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "robobarista object part based transfer of manipulation trajectories from crowd sourcing in 3d pointclouds"

Connected through hyperedge HE-8 (induced by source paper "solar deep structured latent representations for model based reinforcement learning"):
  with members:
    - "variational inverse control with events a general framework for data driven reward definition"
    - "learning latent dynamics for planning from pixels"
    - "learning dexterous in hand manipulation"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "state representation learning for control an overview"
    - "addressing function approximation error in actor critic methods"
    - "model based value estimation for efficient model free 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.LG(Machine Learning)