human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "graspnet a large scale clustered and densely annotated datase for object grasping"):
  Abstract: Object grasping is critical for many applications, which is also a challenging computer vision problem. However, for the clustered scene, current researches suffer from the problems of insufficient training data and the lacking of evaluation benchmarks. In this work, we contribute a large-scale grasp pose detection dataset with a unified evaluation system. Our dataset contains 87,040 RGBD images with over 370 million grasp poses. Meanwhile, our evaluation system directly reports whether a grasping is successful or not by analytic computation, which is able to evaluate any kind of grasp poses without exhausted labeling pose ground-truth. We conduct extensive experiments to show that our dataset and evaluation system can align well with real-world experiments. Our dataset, source code and models will be made publicly available.

Includes members:
  Member "self supervised 6d object pose estimation for robot manipulation":
    also connected through other hyperedges:
      (none)
  Member "s4g amodal single view single shot se 3 grasp detection in cluttered scenes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "roi based robotic grasp detection for object overlapping scenes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "regnet region based grasp network for single shot grasp detection in point clouds")
  Member "6 dof graspnet variational grasp generation for object manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")
  Member "learning task oriented grasping for tool manipulation from simulated self supervision":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "toward sim to real directional semantic grasping")
  Member "closing the loop for robotic grasping a real time generative grasp synthesis approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deceiving image to image translation networks for autonomous driving with adversarial perturbations")
  Member "pointnetgpd detecting grasp configurations from point sets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")
  Member "estimating 6d pose from localizing designated surface keypoints":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a markerless deep learning based 6 degrees of freedom poseestimation for with mobile robots using rgb data")

Question: Please tell me which class the source paper 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.CV(Computer Vision and Pattern Recognition)