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 "cps class level 6d pose and shape estimation from monocular images"):
  Abstract: Contemporary monocular 6D pose estimation methods can only cope with a handful of object instances. This naturally limits possible applications as, for instance, robots need to work with hundreds of different objects in a real environment. In this paper, we propose the first deep learning approach for class-wise monocular 6D pose estimation, coupled with metric shape retrieval. We propose a new loss formulation which directly optimizes over all parameters, i.e. 3D orientation, translation, scale and shape at the same time. Instead of decoupling each parameter, we transform the regressed shape, in the form of a point cloud, to 3D and directly measure its metric misalignment. We experimentally demonstrate that we can retrieve precise metric point clouds from a single image, which can also be further processed for e.g. subsequent rendering. Moreover, we show that our new 3D point cloud loss outperforms all baselines and gives overall good results despite the inherent ambiguity due to monocular data.

Includes members:
  Member "pix2pose pixel wise coordinate regression of objects for 6d pose estimation":
    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 "stereo r cnn based 3d object detection for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "deepsdf learning continuous signed distance functions for shape representation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "disentangling monocular 3d object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "multi path learning for object pose estimation across domains":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "latentfusion end to end differentiable reconstruction and rendering for unseen object pose estimation")
  Member "implicit 3d orientation learning for 6d object detection from rgb images":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deepim deep iterative matching for 6d pose estimation")
  Member "mesh r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")
  Member "normalized object coordinate space for category level 6d object pose and size estimation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")

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)