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 "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.

Includes members:
  Member "pose induction for novel object categories":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "few shot viewpoint estimation")
  Member "inverting visual representations with convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "interpretable cnns for object classification")
  Member "lifting object detection datasets into 3d":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lifting autoencoders unsupervised learning of a fully disentangled 3d morphable model using deep non rigid structure from motion")
  Member "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "tafssl task adaptive feature sub space learning for few shot classification")
  Member "exploring invariances in deep convolutional neural networks using synthetic images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "metamorphic testing for object detection systems")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "viewpoints and keypoints":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mesh r cnn")
  Member "designing deep networks for surface normal estimation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep iterative surface normal 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)