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 "3d convolution on rgb d point clouds for accurate model free object pose estimation"):
  Abstract: The conventional pose estimation of a 3D object usually requires the knowledge of the 3D model of the object. Even with the recent development in convolutional neural networks (CNNs), a 3D model is often necessary in the final estimation. In this paper, we propose a two-stage pipeline that takes in raw colored point cloud data and estimates an object's translation and rotation by running 3D convolutions on voxels. The pipeline is simple yet highly accurate: translation error is reduced to the voxel resolution (around 1 cm) and rotation error is around 5 degrees. The pipeline is also put to actual robotic grasping tests where it achieves above 90% success rate for test objects. Another innovation is that a motion capture system is used to automatically label the point cloud samples which makes it possible to rapidly collect a large amount of highly accurate real data for training the neural networks.

Includes members:
  Member "deep object pose estimation for semantic robotic grasping of household objects":
    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 "transfer learning from synthetic to real images using variational autoencoders for precise position detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "skill transfer in deep reinforcement learning under morphological heterogeneity")
  Member "segicp integrated deep semantic segmentation and pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "teaser fast and certifiable point cloud registration")
  Member "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deepim deep iterative matching for 6d pose estimation")
  Member "multi view 3d object detection network for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "review deep learning on 3d point clouds")
  Member "3d model assisted image segmentation":
    also connected through other hyperedges:
      (none)

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)