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: "jacquard a large scale dataset for robotic grasp detection"
  Abstract: Grasping skill is a major ability that a wide number of real-life applications require for robotisation. State-of-the-art robotic grasping methods perform prediction of object grasp locations based on deep neural networks which require huge amount of labeled data for training and prove impracticable in robotics. In this paper, we propose to generate a large scale synthetic dataset with ground truth, which we refer to as the Jacquard grasping dataset. Specifically, the proposed Jacquard dataset builds on a subset of ShapeNet with its numerous shape objects and features at a scale of millions varied object grasping positions for a large diversity of more than 11k objects. Beyond the simulation of the grasping scene with the underlying object, the ground truth for a successful grasping position is also based on tentatives of a simulated grasping robot. We carried out experiments using an off-the-shelf CNN, with three different evaluation metrics, including real grasping robot trials. The results show that Jacquard enables much better generalization skills than a human labeled dataset thanks to its diversity of objects and grasping positions. For the purpose of reproducible research in robotics, we are releasing along with the Jacquard dataset a web interface for researchers to evaluate the successfulness of their grasping position detections using our dataset.

Connected through hyperedge HE-1 (induced by source paper "regnet region based grasp network for single shot grasp detection in point clouds"):
  with members:
    - "object detection in 20 years a survey"
    - "relation shape convolutional neural network for point cloud analysis"
    - "roi based robotic grasp detection for object overlapping scenes"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "pointnetgpd detecting grasp configurations from point sets"
    - "a real time robotic grasp approach with oriented anchor box"
    - "fully convolutional grasp detection network with oriented anchor box"
    - "so net self organizing network for point cloud analysis"

Connected through hyperedge HE-2 (induced by source paper "egad an evolved grasping analysis dataset for diversity and reproducibility in robotic manipulation"):
  with members:
    - "pointnetgpd detecting grasp configurations from point sets"
    - "towards the targeted environment specific evolution of robot components"
    - "fully convolutional grasp detection network with oriented anchor box"
    - "diversity is all you need learning skills without a reward function"
    - "densely supervised grasp detector dsgd"
    - "multi view picking next best view reaching for improved grasping in clutter"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "grasp pose detection in point clouds"

Connected through hyperedge HE-3 (induced by source paper "optimizing correlated graspability score and grasp regression for better grasp prediction"):
  with members:
    - "fully convolutional grasp detection network with oriented anchor box"
    - "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"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-4 (induced by source paper "robotic grasp manipulation using evolutionary computing and deep reinforcement learning"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "yolov3 an incremental improvement"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "deep q networks for accelerating the training of deep neural networks"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"
    - "deep learning for detecting robotic grasps"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-5 (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"
    - "closing the loop for robotic grasping a real time generative grasp synthesis approach"
    - "pointnetgpd detecting grasp configurations from point sets"
    - "estimating 6d pose from localizing designated surface keypoints"

Connected through hyperedge HE-6 (induced by source paper "gq stn optimizing one shot grasp detection based on robustness classifier"):
  with members:
    - "dealing with ambiguity in robotic grasping via multiple predictions"
    - "rotation ensemble module for detecting rotation invariant features"
    - "fully convolutional grasp detection network with oriented anchor box"
    - "roi based robotic grasp detection in object overlapping scenes using convolutional neural network"
    - "classification based grasp detection using spatial transformer network"
    - "learning object localization and 6d pose estimation from simulation and weakly labeled real images"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "robotic grasp detection using deep convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "antipodal robotic grasping using generative residual convolutional neural network"):
  with members:
    - "learning probabilistic multi modal actor models for vision based robotic grasping"
    - "data efficient learning for sim to real robotic grasping using deep point cloud prediction networks"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "fully convolutional grasp detection network with oriented anchor box"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "depth image inpainting improving low rank matrix completion with low gradient regularization"

Connected through hyperedge HE-8 (induced by source paper "roi based robotic grasp detection for object overlapping scenes"):
  with members:
    - "planning multi fingered grasps as probabilistic inference in a learned deep network"
    - "visual manipulation relationship network"
    - "dealing with ambiguity in robotic grasping via multiple predictions"
    - "fully convolutional grasp detection network with oriented anchor box"
    - "real world multi object multi grasp detection"
    - "robotic grasp detection using deep convolutional neural networks"
    - "high precision grasp pose detection in dense clutter"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"

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)