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: "learning visual servoing with deep features and fitted q iteration"
  Abstract: Visual servoing involves choosing actions that move a robot in response to observations from a camera, in order to reach a goal configuration in the world. Standard visual servoing approaches typically rely on manually designed features and analytical dynamics models, which limits their generalization capability and often requires extensive application-specific feature and model engineering. In this work, we study how learned visual features, learned predictive dynamics models, and reinforcement learning can be combined to learn visual servoing mechanisms. We focus on target following, with the goal of designing algorithms that can learn a visual servo using low amounts of data of the target in question, to enable quick adaptation to new targets. Our approach is based on servoing the camera in the space of learned visual features, rather than image pixels or manually-designed keypoints. We demonstrate that standard deep features, in our case taken from a model trained for object classification, can be used together with a bilinear predictive model to learn an effective visual servo that is robust to visual variation, changes in viewing angle and appearance, and occlusions. A key component of our approach is to use a sample-efficient fitted Q-iteration algorithm to learn which features are best suited for the task at hand. We show that we can learn an effective visual servo on a complex synthetic car following benchmark using just 20 training trajectory samples for reinforcement learning. We demonstrate substantial improvement over a conventional approach based on image pixels or hand-designed keypoints, and we show an improvement in sample-efficiency of more than two orders of magnitude over standard model-free deep reinforcement learning algorithms. Videos are available at this http URL .

Connected through hyperedge HE-1 (induced by source paper "acceleration of actor critic deep reinforcement learning for visual grasping in clutter by state representation learning based on disentanglement of a raw input image"):
  with members:
    - "stochastic latent actor critic deep reinforcement learning with a latent variable model"
    - "domain randomization for active pose estimation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "deepmind control suite"
    - "learning latent dynamics for planning from pixels"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-2 (induced by source paper "dfvs deep flow guided scene agnostic image based visual servoing"):
  with members:
    - "siamese convolutional neural network for sub millimeter accurate camera pose estimation and visual servoing"
    - "robustness via retrying closed loop robotic manipulation with self supervised learning"
    - "high quality monocular depth estimation via transfer learning"
    - "exploring convolutional networks for end to end visual servoing"
    - "sim2real view invariant visual servoing by recurrent control"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "densely connected convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "divis domain invariant visual servoing for collision free goal reaching"):
  with members:
    - "vr goggles for robots real to sim domain adaptation for visual control"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "zero shot visual imitation"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "speaker follower models for vision and language navigation"
    - "viser visual self regularization"
    - "building generalizable agents with a realistic and rich 3d environment"

Connected through hyperedge HE-4 (induced by source paper "morphology agnostic visual robotic control"):
  with members:
    - "replab a reproducible low cost arm benchmark platform for robotic learning"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "contingency aware exploration in reinforcement learning"
    - "towards a generic diver following algorithm balancing robustness and efficiency in deep visual detection"
    - "self supervised visual planning with temporal skip connections"
    - "underwater multi robot convoying using visual tracking by detection"
    - "learning a visuomotor controller for real world robotic grasping using easily simulated depth images"
    - "sim2real view invariant visual servoing by recurrent control"

Connected through hyperedge HE-5 (induced by source paper "a deep learning approach for robust corridor following"):
  with members:
    - "exploring convolutional networks for end to end visual servoing"
    - "deep residual learning for image recognition"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "visual reinforcement learning with imagined goals"):
  with members:
    - "universal planning networks"
    - "temporal difference models model free deep rl for model based control"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "addressing function approximation error in actor critic methods"
    - "diversity is all you need learning skills without a reward function"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "self supervised visual planning with temporal skip connections"
    - "pves position velocity encoders for unsupervised learning of structured state representations"

Connected through hyperedge HE-7 (induced by source paper "zero shot visual imitation"):
  with members:
    - "self supervised visual planning with temporal skip connections"
    - "hindsight experience replay"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "time contrastive networks self supervised learning from video"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "curiosity driven exploration by self supervised prediction"
    - "one shot imitation learning"
    - "generative adversarial imitation learning"

Connected through hyperedge HE-8 (induced by source paper "cloth manipulation using random forest based imitation learning"):
  with members:
    - "cloth manipulation using random forest based controller parametrization"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "manipulating highly deformable materials using a visual feedback dictionary"
    - "learning dexterous manipulation for a soft robotic hand from human demonstration"
    - "multi sensor surface analysis for robotic ironing"
    - "folding deformable objects using predictive simulation and trajectory optimization"
    - "end to end training of deep visuomotor policies"
    - "a reduction of imitation learning and structured prediction to no regret online 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)