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: "model free trajectory optimization for reinforcement learning"
  Abstract: Many of the recent Trajectory Optimization algorithms alternate between local approximation of the dynamics and conservative policy update. However, linearly approximating the dynamics in order to derive the new policy can bias the update and prevent convergence to the optimal policy. In this article, we propose a new model-free algorithm that backpropagates a local quadratic time-dependent Q-Function, allowing the derivation of the policy update in closed form. Our policy update ensures exact KL-constraint satisfaction without simplifying assumptions on the system dynamics demonstrating improved performance in comparison to related Trajectory Optimization algorithms linearizing the dynamics.

Connected through hyperedge HE-1 (induced by source paper "td regularized actor critic methods"):
  with members:
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "rainbow combining improvements in deep reinforcement learning"
    - "deep reinforcement learning that matters"
    - "f divergence constrained policy improvement"
    - "trust pcl an off policy trust region method for continuous control"
    - "boosting the actor with dual critic"

Connected through hyperedge HE-2 (induced by source paper "compatible natural gradient policy search"):
  with members:
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "carla an open urban driving simulator"
    - "asynchronous methods for deep reinforcement learning"
    - "benchmarking deep reinforcement learning for continuous control"
    - "model free trajectory based policy optimization with monotonic improvement"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"
    - "online planning algorithms for pomdps"

Connected through hyperedge HE-3 (induced by source paper "a decoupled data based approach to stochastic optimal control problems"):
  with members:
    - "deepmind control suite"
    - "distributed distributional deterministic policy gradients"

Connected through hyperedge HE-4 (induced by source paper "combining model based and model free updates for trajectory centric reinforcement learning"):
  with members:
    - "guided policy search as approximate mirror descent"
    - "continuous deep q learning with model based acceleration"
    - "path integral guided policy search"
    - "reset free guided policy search efficient deep reinforcement learning with stochastic initial states"
    - "high dimensional continuous control using generalized advantage estimation"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"
    - "learning continuous control policies by stochastic value gradients"

Connected through hyperedge HE-5 (induced by source paper "a separation based design to data driven control for large scale partially observed systems"):
  with members:
    - "a near optimal separation principle for nonlinear stochastic systems arising in robotic path planning and control"
    - "near optimal belief space planning via t lqg"
    - "belief space planning simplified trajectory optimized lqg t lqg extended report"

Connected through hyperedge HE-6 (induced by source paper "stochastic feedback control of systems with unknown nonlinear dynamics"):
  with members:
    - "infinite horizon policy gradient estimation"

Connected through hyperedge HE-7 (induced by source paper "towards lifelong self supervision a deep learning direction for robotics"):
  with members:
    - "end to end tracking and semantic segmentation using recurrent neural networks"
    - "3d simulation for robot arm control with deep q learning"
    - "deep learning a grasp function for grasping under gripper pose uncertainty"
    - "classifying options for deep reinforcement learning"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "intrinsically motivated multimodal structure learning"
    - "visualizing deep convolutional neural networks using natural pre images"

Connected through hyperedge HE-8 (induced by source paper "reset free guided policy search efficient deep reinforcement learning with stochastic initial states"):
  with members:
    - "guided policy search as approximate mirror descent"
    - "benchmarking deep reinforcement learning for continuous control"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"
    - "end to end training of deep visuomotor policies"

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)