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: "symmetric non rigid structure from motion for category specific object structure estimation"
  Abstract: Many objects, especially these made by humans, are symmetric, e.g. cars and aeroplanes. This paper addresses the estimation of 3D structures of symmetric objects from multiple images of the same object category, e.g. different cars, seen from various viewpoints. We assume that the deformation between different instances from the same object category is non-rigid and symmetric. In this paper, we extend two leading non-rigid structure from motion (SfM) algorithms to exploit symmetry constraints. We model the both methods as energy minimization, in which we also recover the missing observations caused by occlusions. In particularly, we show that by rotating the coordinate system, the energy can be decoupled into two independent terms, which still exploit symmetry, to apply matrix factorization separately on each of them for initialization. The results on the Pascal3D+ dataset show that our methods significantly improve performance over baseline methods.

Connected through hyperedge HE-1 (induced by source paper "unsupervised learning of category specific symmetric 3d keypoints from point sets"):
  with members:
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "in perfect shape certifiably optimal 3d shape reconstruction from 2d landmarks"
    - "c3dpo canonical 3d pose networks for non rigid structure from motion"
    - "cycle in cycle generative adversarial networks for keypoint guided image generation"
    - "deep non rigid structure from motion"
    - "pytorch an imperative style high performance deep learning library"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"

Connected through hyperedge HE-2 (induced by source paper "semi supervised sparse representation based classification for face recognition with insufficient labeled samples"):
  with members:
    - "exploiting symmetry and or manhattan properties for 3d object structure estimation from single and multiple images"
    - "sparse illumination learning and transfer for single sample face recognition with image corruption and misalignment"
    - "constructing a non negative low rank and sparse graph with data adaptive features"
    - "deeply learned face representations are sparse selective and robust"
    - "dynamic updating for ell_ 1 minimization"

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)