human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "pose estimation for objects with rotational symmetry"
  Abstract: Pose estimation is a widely explored problem, enabling many robotic tasks such as grasping and manipulation. In this paper, we tackle the problem of pose estimation for objects that exhibit rotational symmetry, which are common in man-made and industrial environments. In particular, our aim is to infer poses for objects not seen at training time, but for which their 3D CAD models are available at test time. Previous work has tackled this problem by learning to compare captured views of real objects with the rendered views of their 3D CAD models, by embedding them in a joint latent space using neural networks. We show that sidestepping the issue of symmetry in this scenario during training leads to poor performance at test time. We propose a model that reasons about rotational symmetry during training by having access to only a small set of symmetry-labeled objects, whereby exploiting a large collection of unlabeled CAD models. We demonstrate that our approach significantly outperforms a naively trained neural network on a new pose dataset containing images of tools and hardware.

Connected through hyperedge HE-1 (induced by source paper "enabling viewpoint learning through dynamic label generation"):
  with members:
    - "spherical regression learning viewpoints surface normals and 3d rotations on n spheres"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "cnns based viewpoint estimation for volume visualization"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "viewpoint selection for photographing architectures"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-2 (induced by source paper "on object symmetries and 6d pose estimation from images"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "bop benchmark for 6d object pose estimation"
    - "defining the pose of any 3d rigid object and an associated distance"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "pvnet pixel wise voting network for 6dof pose estimation"
    - "learning to generate and reconstruct 3d meshes with only 2d supervision"
    - "deep 6dpose recovering 6d object pose from a single rgb image"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"

Connected through hyperedge HE-3 (induced by source paper "explaining the ambiguity of object detection and 6d pose from visual data"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "deep model based 6d pose refinement in rgb"
    - "deep 6dpose recovering 6d object pose from a single rgb image"
    - "roi 10d monocular lifting of 2d detection to 6d pose and metric shape"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "wavelet based reflection symmetry detection via textural and color histograms"
    - "srn side output residual network for object symmetry detection in the wild"

Question: Please tell me which class the center node 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)