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: "low rank representation on grassmann manifolds an extrinsic perspective"
  Abstract: Many computer vision algorithms employ subspace models to represent data. The Low-rank representation (LRR) has been successfully applied in subspace clustering for which data are clustered according to their subspace structures. The possibility of extending LRR on Grassmann manifold is explored in this paper. Rather than directly embedding Grassmann manifold into a symmetric matrix space, an extrinsic view is taken by building the self-representation of LRR over the tangent space of each Grassmannian point. A new algorithm for solving the proposed Grassmannian LRR model is designed and implemented. Several clustering experiments are conducted on handwritten digits dataset, dynamic texture video clips and YouTube celebrity face video data. The experimental results show our method outperforms a number of existing methods.

Connected through hyperedge HE-1 (induced by source paper "scalable dense non rigid structure from motion a grassmannian perspective"):
  with members:
    - "a benchmark and evaluation of non rigid structure from motion"
    - "monocular dense 3d reconstruction of a complex dynamic scene from two perspective frames"
    - "dense non rigid structure from motion made easy a spatial temporal smoothness based solution"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-2 (induced by source paper "kernelized lrr on grassmann manifolds for subspace clustering"):
  with members:
    - "expanding the family of grassmannian kernels an embedding perspective"
    - "optimizing over radial kernels on compact manifolds"
    - "dictionary learning and sparse coding on grassmann manifolds an extrinsic solution"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "robust principal component analysis"
    - "a tutorial on spectral clustering"
    - "principal manifolds and nonlinear dimension reduction via local tangent space alignment"

Connected through hyperedge HE-3 (induced by source paper "kernel sparse subspace clustering on symmetric positive definite manifolds"):
  with members:
    - "kernelized low rank representation on grassmann manifolds"
    - "kernel methods on the riemannian manifold of symmetric positive definite matrices"
    - "sparse coding on symmetric positive definite manifolds using bregman divergences"
    - "linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine learning"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse coding and dictionary learning for symmetric positive definite matrices a kernel approach"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-4 (induced by source paper "image segmentation with superpixel based covariance descriptors in low rank representation"):
  with members:
    - "kernelized low rank representation on grassmann manifolds"
    - "kernel methods on riemannian manifolds with gaussian rbf kernels"
    - "robust recovery of subspace structures by low rank representation"
    - "robust principal component analysis"

Connected through hyperedge HE-5 (induced by source paper "low rank representation over the manifold of curves"):
  with members:
    - "video based action recognition using rate invariant analysis of covariance trajectories"
    - "linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine learning"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-6 (induced by source paper "neighborhood preserved sparse representation for robust classification on symmetric positive definite matrices"):
  with members:
    - "sparse coding on symmetric positive definite manifolds using bregman divergences"
    - "linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine learning"

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)