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 "spherical regression learning viewpoints surface normals and 3d rotations on n spheres"):
  Abstract: Many computer vision challenges require continuous outputs, but tend to be solved by discrete classification. The reason is classification's natural containment within a probability $n$-simplex, as defined by the popular softmax activation function. Regular regression lacks such a closed geometry, leading to unstable training and convergence to suboptimal local minima. Starting from this insight we revisit regression in convolutional neural networks. We observe many continuous output problems in computer vision are naturally contained in closed geometrical manifolds, like the Euler angles in viewpoint estimation or the normals in surface normal estimation. A natural framework for posing such continuous output problems are $n$-spheres, which are naturally closed geometric manifolds defined in the $\mathbb{R}^{(n+1)}$ space. By introducing a spherical exponential mapping on $n$-spheres at the regression output, we obtain well-behaved gradients, leading to stable training. We show how our spherical regression can be utilized for several computer vision challenges, specifically viewpoint estimation, surface normal estimation and 3D rotation estimation. For all these problems our experiments demonstrate the benefit of spherical regression. All paper resources are available at this https URL.

Includes members:
  Member "3d pose estimation and 3d model retrieval for objects in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised deep metric learning via auxiliary rotation loss")
  Member "a mixed classification regression framework for 3d pose estimation from 2d images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a multi hypothesis approach to color constancy")
  Member "viewpoint estimation insights model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint viewpoint and keypoint estimation with real and synthetic data")
  Member "deep directional statistics pose estimation with uncertainty quantification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mixture dense regression for object detection and human pose estimation")
  Member "3d pose regression using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "6d object pose regression via supervised learning on point clouds")
  Member "geometric loss functions for camera pose regression with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "3d bounding box estimation using deep learning and geometry":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "siamese instance search for tracking":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")

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)