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: "path integral guided policy search"
  Abstract: We present a policy search method for learning complex feedback control policies that map from high-dimensional sensory inputs to motor torques, for manipulation tasks with discontinuous contact dynamics. We build on a prior technique called guided policy search (GPS), which iteratively optimizes a set of local policies for specific instances of a task, and uses these to train a complex, high-dimensional global policy that generalizes across task instances. We extend GPS in the following ways: (1) we propose the use of a model-free local optimizer based on path integral stochastic optimal control (PI2), which enables us to learn local policies for tasks with highly discontinuous contact dynamics; and (2) we enable GPS to train on a new set of task instances in every iteration by using on-policy sampling: this increases the diversity of the instances that the policy is trained on, and is crucial for achieving good generalization. We show that these contributions enable us to learn deep neural network policies that can directly perform torque control from visual input. We validate the method on a challenging door opening task and a pick-and-place task, and we demonstrate that our approach substantially outperforms the prior LQR-based local policy optimizer on these tasks. Furthermore, we show that on-policy sampling significantly increases the generalization ability of these policies.

Connected through hyperedge HE-1 (induced by source paper "local policy optimization for trajectory centric reinforcement learning"):
  with members:
    - "benchmarking model based reinforcement learning"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "guided policy search as approximate mirror descent"
    - "end to end training of deep visuomotor policies"
    - "control design along trajectories with sums of squares programming"

Connected through hyperedge HE-2 (induced by source paper "robot playing kendama with model based and model free reinforcement learning"):
  with members:
    - "smooth and efficient policy exploration for robot trajectory learning"
    - "combining model based and model free updates for trajectory centric reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "competitive experience replay"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "backplay man muss immer umkehren"
    - "learning dexterous in hand manipulation"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "large scale study of curiosity driven learning"
    - "forward backward reinforcement learning"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "barc backward reachability curriculum for robotic reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "making sense of vision and touch learning multimodal representations for contact rich tasks"):
  with members:
    - "fundamental limitations in performance and interpretability of common planar rigid body contact models"
    - "joint inference of kinematic and force trajectories with visuo tactile sensing"
    - "from variational to deterministic autoencoders"
    - "manipulation by feel touch based control with deep predictive models"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "learning latent space dynamics for tactile servoing"

Connected through hyperedge HE-5 (induced by source paper "motion planning networks bridging the gap between learning based and classical motion planners"):
  with members:
    - "neural path planning fixed time near optimal path generation via oracle imitation"
    - "composing task agnostic policies with deep reinforcement learning"
    - "composing ensembles of policies with deep reinforcement learning"
    - "robot motion planning in learned latent spaces"
    - "universal planning networks"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "adversarial imitation via variational inverse reinforcement learning"
    - "experience replay for continual learning"

Connected through hyperedge HE-6 (induced by source paper "self supervised correspondence in visuomotor policy learning"):
  with members:
    - "unsupervised learning of object keypoints for perception and control"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "graph structured visual imitation"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "unsupervised visuomotor control through distributional planning networks"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"

Connected through hyperedge HE-7 (induced by source paper "skew fit state covering self supervised reinforcement learning"):
  with members:
    - "self supervised learning of image embedding for continuous control"
    - "provably efficient maximum entropy exploration"
    - "exploration by random network distillation"
    - "temporal difference models model free deep rl for model based control"
    - "unsupervised meta learning for reinforcement learning"
    - "visual reinforcement learning with imagined goals"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "large scale study of curiosity driven learning"

Connected through hyperedge HE-8 (induced by source paper "a survey of behavior learning applications in robotics state of the art and perspectives"):
  with members:
    - "learning dexterous in hand manipulation"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "deep haptic model predictive control for robot assisted dressing"
    - "one shot visual imitation learning via meta learning"
    - "deep reinforcement learning a brief survey"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "combining model based and model free updates for trajectory centric reinforcement learning"
    - "time contrastive networks self supervised learning from video"

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.RO(Robotics)