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: "shape and symmetry induction for 3d objects"
  Abstract: Actions as simple as grasping an object or navigating around it require a rich understanding of that object's 3D shape from a given viewpoint. In this paper we repurpose powerful learning machinery, originally developed for object classification, to discover image cues relevant for recovering the 3D shape of potentially unfamiliar objects. We cast the problem as one of local prediction of surface normals and global detection of 3D reflection symmetry planes, which open the door for extrapolating occluded surfaces from visible ones. We demonstrate that our method is able to recover accurate 3D shape information for classes of objects it was not trained on, in both synthetic and real images.

Connected through hyperedge HE-1 (induced by source paper "pose estimation for objects with rotational symmetry"):
  with members:
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "symmetry aware evaluation of 3d object detection and pose estimation in scenes of many parts in bulk"
    - "exploiting cyclic symmetry in convolutional neural networks"
    - "end to end learning of deep visual representations for image retrieval"
    - "deep learning of local rgb d patches for 3d object detection and 6d pose estimation"
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"
    - "siamese regression networks with efficient mid level feature extraction for 3d object pose estimation"

Connected through hyperedge HE-2 (induced by source paper "hybrid bayesian eigenobjects combining linear subspace and deep network methods for 3d robot vision"):
  with members:
    - "shape completion enabled robotic grasping"
    - "fusionnet 3d object classification using multiple data representations"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "gift a real time and scalable 3d shape search engine"
    - "deep sliding shapes for amodal 3d object detection in rgb d images"
    - "convolutional models for joint object categorization and pose estimation"
    - "3d shapenets a deep representation for volumetric shapes"
    - "viewpoints and keypoints"

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)