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 in object overlapping scenes using convolutional neural network"):
  Abstract: Grasp detection is an essential skill for widespread use of robots. Recent works demonstrate the advanced performance of Convolutional Neural Network (CNN) on robotic grasp detection. However, a significant shortcoming of existing grasp detection algorithms is that they all ignore the affiliation between grasps and targets. In this paper, we propose a robotic grasp detection algorithm based on Region of Interest (RoI) to simultaneously detect targets and their grasps in object overlapping scenes. Our proposed algorithm uses Regions of Interest (RoIs) to detect grasps while doing classification and location regression of targets. To train the network, we contribute a much bigger multi-object grasp dataset than Cornell Grasp Dataset, which is based on Visual Manipulation Relationship Dataset. Experimental results demonstrate that our algorithm achieves 24.9% miss rate at 1FPPI and 68.2% mAP with grasp on our dataset. Robotic experiments demonstrate that our proposed algorithm can help robots grasp specified target in multi-object scenes at 84% success rate.

Includes members:
  Member "visual manipulation relationship network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "roi based robotic grasp detection for object overlapping scenes")
  Member "real world multi object multi grasp detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "roi based robotic grasp detection for object overlapping scenes")
  Member "jacquard a large scale dataset for robotic grasp detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "regnet region based grasp network for single shot grasp detection in point clouds")
  Member "dex net 3 0 computing robust robot suction grasp targets in point clouds using a new analytic model and deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "robotic pick and place of novel objects in clutter with multi affordance grasping and cross domain image matching":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "robotic grasp detection using deep convolutional neural networks":
    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 "high precision grasp pose detection in dense clutter":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "regnet region based grasp network for single shot grasp detection in point clouds")
  Member "learning hand eye coordination for robotic grasping with deep learning and large scale data collection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning")

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)