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 "a self supervised learning system for object detection using physics simulation and multi view pose estimation"):
  Abstract: Progress has been achieved recently in object detection given advancements in deep learning. Nevertheless, such tools typically require a large amount of training data and significant manual effort to label objects. This limits their applicability in robotics, where solutions must scale to a large number of objects and variety of conditions. This work proposes an autonomous process for training a Convolutional Neural Network (CNN) for object detection and pose estimation in robotic setups. The focus is on detecting objects placed in cluttered, tight environments, such as a shelf with multiple objects. In particular, given access to 3D object models, several aspects of the environment are physically simulated. The models are placed in physically realistic poses with respect to their environment to generate a labeled synthetic dataset. To further improve object detection, the network self-trains over real images that are labeled using a robust multi-view pose estimation process. The proposed training process is evaluated on several existing datasets and on a dataset collected for this paper with a Motoman robotic arm. Results show that the proposed approach outperforms popular training processes relying on synthetic - but not physically realistic - data and manual annotation. The key contributions are the incorporation of physical reasoning in the synthetic data generation process and the automation of the annotation process over real images.

Includes members:
  Member "self paced deep learning for weakly supervised object detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "low budget unsupervised label query through domain alignment enforcement")
  Member "high quality tabletop rearrangement with overhand grasps hardness results and fast methods":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi object rearrangement with monte carlo tree search a case study on planar nonprehensile sorting")
  Member "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deepim deep iterative matching for 6d pose estimation")
  Member "team delft s robot winner of the amazon picking challenge 2016":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "instance segmentation of visible and occluded regions for finding and picking target from a pile of objects")
  Member "analysis and observations from the first amazon picking challenge":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust 6d object pose estimation by learning rgb d features")
  Member "fully convolutional instance aware semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep affinity net instance segmentation via affinity")
  Member "webly supervised learning of convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)