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: "closing the loop for robotic grasping a real time generative grasp synthesis approach"
  Abstract: This paper presents a real-time, object-independent grasp synthesis method which can be used for closed-loop grasping. Our proposed Generative Grasping Convolutional Neural Network (GG-CNN) predicts the quality and pose of grasps at every pixel. This one-to-one mapping from a depth image overcomes limitations of current deep-learning grasping techniques by avoiding discrete sampling of grasp candidates and long computation times. Additionally, our GG-CNN is orders of magnitude smaller while detecting stable grasps with equivalent performance to current state-of-the-art techniques. The light-weight and single-pass generative nature of our GG-CNN allows for closed-loop control at up to 50Hz, enabling accurate grasping in non-static environments where objects move and in the presence of robot control inaccuracies. In our real-world tests, we achieve an 83% grasp success rate on a set of previously unseen objects with adversarial geometry and 88% on a set of household objects that are moved during the grasp attempt. We also achieve 81% accuracy when grasping in dynamic clutter.

Connected through hyperedge HE-1 (induced by source paper "deceiving image to image translation networks for autonomous driving with adversarial perturbations"):
  with members:
    - "yes we gan applying adversarial techniques for autonomous driving"
    - "understanding adversarial attacks on deep learning based medical image analysis systems"
    - "data augmentation with symbolic to real image translation gans for traffic sign recognition"
    - "spatially transformed adversarial examples"
    - "real to virtual domain unification for end to end autonomous driving"
    - "multimodal unsupervised image to image translation"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-2 (induced by source paper "learning pregrasp manipulation of objects from ungraspable poses"):
  with members:
    - "robust execution of contact rich motion plans by hybrid force velocity control"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "learning dexterous in hand manipulation"
    - "pointnetgpd detecting grasp configurations from point sets"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "randomized physics based motion planning for grasping in cluttered and uncertain environments"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"

Connected through hyperedge HE-3 (induced by source paper "semi supervised grasp detection by representation learning in a vector quantized latent space"):
  with members:
    - "an introduction to variational autoencoders"
    - "neural discrete representation learning"
    - "robotic grasp detection using deep convolutional neural networks"
    - "associating grasping with convolutional neural network features"
    - "deep residual learning for image recognition"
    - "real time grasp detection using convolutional neural networks"
    - "semi supervised learning with deep generative models"
    - "show and tell a neural image caption generator"

Connected through hyperedge HE-4 (induced by source paper "mechanical search multi step retrieval of a target object occluded by clutter"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "one shot segmentation in clutter"
    - "segmenting unknown 3d objects from real depth images using mask r cnn trained on synthetic data"
    - "visual representations for semantic target driven navigation"
    - "randomized physics based motion planning for grasping in cluttered and uncertain environments"
    - "interactive perception leveraging action in perception and perception in action"
    - "neural task programming learning to generalize across hierarchical tasks"

Connected through hyperedge HE-5 (induced by source paper "kpam sc generalizable manipulation planning using keypoint affordance and shape completion"):
  with members:
    - "inferring occluded geometry improves performance when retrieving an object from dense clutter"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "object placement planning and optimization for robot manipulators"
    - "robust grasp planning over uncertain shape completions"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "category level 6d object pose recovery in depth images"

Connected through hyperedge HE-6 (induced by source paper "graspnet a large scale clustered and densely annotated datase for object grasping"):
  with members:
    - "self supervised 6d object pose estimation for robot manipulation"
    - "s4g amodal single view single shot se 3 grasp detection in cluttered scenes"
    - "roi based robotic grasp detection for object overlapping scenes"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "pointnetgpd detecting grasp configurations from point sets"
    - "estimating 6d pose from localizing designated surface keypoints"
    - "roi based robotic grasp detection in object overlapping scenes using convolutional neural network"

Connected through hyperedge HE-7 (induced by source paper "deep robotic prediction with hierarchical rgb d fusion"):
  with members:
    - "deep surface normal estimation with hierarchical rgb d fusion"
    - "learning robotic assembly from cad"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "learning to represent haptic feedback for partially observable tasks"
    - "grasp pose detection in point clouds"
    - "learning end to end multimodal sensor policies for autonomous navigation"

Connected through hyperedge HE-8 (induced by source paper "a single multi task deep neural network with post processing for object detection with reasoning and robotic grasp detection"):
  with members:
    - "roi based robotic grasp detection for object overlapping scenes"
    - "yolov3 an incremental improvement"
    - "fully convolutional grasp detection network with oriented anchor box"
    - "roi based robotic grasp detection in object overlapping scenes using convolutional neural network"
    - "focal loss for dense object detection"
    - "dex net 3 0 computing robust robot suction grasp targets in point clouds using a new analytic model and deep learning"
    - "robotic grasp detection using deep convolutional neural networks"
    - "deep learning a grasp function for grasping under gripper pose uncertainty"

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)