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 "vision based picking system for automatic express package dispatching"):
  Abstract: This paper presents a vision based robotic system to handle the picking problem involved in automatic express package dispatching. By utilizing two RealSense RGB-D cameras and one UR10 industrial robot, package dispatching task which is usually done by human can be completed automatically. In order to determine grasp point for overlapped deformable objects, we improved the sampling algorithm proposed by the group in Berkeley to directly generate grasp candidate from depth images. For the purpose of package recognition, the deep network framework YOLO is integrated. We also designed a multi-modal robot hand composed of a two-fingered gripper and a vacuum suction cup to deal with different kinds of packages. All the technologies have been integrated in a work cell which simulates the practical conditions of an express package dispatching scenario. The proposed system is verified by experiments conducted for two typical express items.

Includes members:
  Member "grasp pose detection in point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")
  Member "design of a multi modal end effector and grasping system how integrated design helped win the amazon robotics challenge":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "video object segmentation based visual servo control and object depth estimation on a mobile robot platform")
  Member "cartman the low cost cartesian manipulator that won the amazon robotics challenge":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pointins point based instance segmentation")
  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 "high precision grasp pose detection in dense clutter":
    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 "yolo9000 better faster stronger":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "a summary of team mit s approach to the amazon picking challenge 2015":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "development of a robotic system for automated decaking of 3d printed parts")

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)