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 "convposecnn dense convolutional 6d object pose estimation"):
  Abstract: 6D object pose estimation is a prerequisite for many applications. In recent years, monocular pose estimation has attracted much research interest because it does not need depth measurements. In this work, we introduce ConvPoseCNN, a fully convolutional architecture that avoids cutting out individual objects. Instead we propose pixel-wise, dense prediction of both translation and orientation components of the object pose, where the dense orientation is represented in Quaternion form. We present different approaches for aggregation of the dense orientation predictions, including averaging and clustering schemes. We evaluate ConvPoseCNN on the challenging YCB-Video Dataset, where we show that the approach has far fewer parameters and trains faster than comparable methods without sacrificing accuracy. Furthermore, our results indicate that the dense orientation prediction implicitly learns to attend to trustworthy, occlusion-free, and feature-rich object regions.

Includes members:
  Member "deepim deep iterative matching for 6d pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "deep object pose estimation for semantic robotic grasping of household objects":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")
  Member "pvnet pixel wise voting network for 6dof pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cps class level 6d pose and shape estimation from monocular images")
  Member "deep 6dpose recovering 6d object pose from a single rgb image":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning canonical shape space for category level 6d object pose and size estimation")
  Member "making deep heatmaps robust to partial occlusions for 3d object pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust 6d object pose estimation by learning rgb d features")
  Member "focal loss for dense object detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "ipose instance aware 6d pose estimation of partly occluded objects":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "6d object pose regression via supervised learning on point clouds")
  Member "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "neural mesh refiner for 6 dof pose estimation")

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.CV(Computer Vision and Pattern Recognition)