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 "roi based robotic grasp detection for object overlapping scenes"):
  Abstract: Grasp detection with consideration of the affiliations between grasps and their owner in object overlapping scenes is a necessary and challenging task for the practical use of the robotic grasping approach. In this paper, a robotic grasp detection algorithm named ROI-GD is proposed to provide a feasible solution to this problem based on Region of Interest (ROI), which is the region proposal for objects. ROI-GD uses features from ROIs to detect grasps instead of the whole scene. It has two stages: the first stage is to provide ROIs in the input image and the second-stage is the grasp detector based on ROI features. We also contribute a multi-object grasp dataset, which is much larger than Cornell Grasp Dataset, by labeling Visual Manipulation Relationship Dataset. Experimental results demonstrate that ROI-GD performs much better in object overlapping scenes and at the meantime, remains comparable with state-of-the-art grasp detection algorithms on Cornell Grasp Dataset and Jacquard Dataset. Robotic experiments demonstrate that ROI-GD can help robots grasp the target in single-object and multi-object scenes with the overall success rates of 92.5% and 83.8% respectively.

Includes members:
  Member "planning multi fingered grasps as probabilistic inference in a learned deep network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi fingered grasp planning via inference in deep neural networks")
  Member "visual manipulation relationship network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "roi based robotic grasp detection in object overlapping scenes using convolutional neural network")
  Member "dealing with ambiguity in robotic grasping via multiple predictions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gq stn optimizing one shot grasp detection based on robustness classifier")
  Member "fully convolutional grasp detection network with oriented anchor box":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "regnet region based grasp network for single shot grasp detection in point clouds")
  Member "real world multi object multi grasp detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "roi based robotic grasp detection in object overlapping scenes using convolutional neural network")
  Member "jacquard a large scale dataset for robotic grasp detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "regnet region based grasp network for single shot grasp detection in point clouds")
  Member "robotic grasp detection using deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")
  Member "high precision grasp pose detection in dense clutter":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "regnet region based grasp network for single shot grasp detection in point clouds")

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.RO(Robotics)