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: "sparse subspace clustering by orthogonal matching pursuit"
  Abstract: Subspace clustering methods based on $\ell_1$, $\ell_2$ or nuclear norm regularization have become very popular due to their simplicity, theoretical guarantees and empirical success. However, the choice of the regularizer can greatly impact both theory and practice. For instance, $\ell_1$ regularization is guaranteed to give the correct clustering under broad conditions (e.g., arbitrary subspaces and corrupted data), but requires solving a large scale convex optimization problem. On the other hand, $\ell_2$ and nuclear norm regularization provide efficient closed form solutions, but require very strong assumptions to guarantee the correct clustering (e.g., independent subspaces and uncorrupted data). This paper proposes a new subspace clustering method based on orthogonal matching pursuit that is computationally efficient and guaranteed to provide the correct clustering for arbitrary subspaces. Experiments on synthetic data verify our theoretical analysis, and applications in handwritten digit and face clustering show that our approach achieves the best trade off between accuracy and efficiency. Moreover, our approach is the only one that can handle 100,000 points.

Connected through hyperedge HE-1 (induced by source paper "scalable sparse subspace clustering by orthogonal matching pursuit"):
  with members:
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "correlation adaptive subspace segmentation by trace lasso"
    - "robust subspace clustering"
    - "robust and efficient subspace segmentation via least squares regression"
    - "robust recovery of subspace structures by low rank representation"
    - "greedy feature selection for subspace clustering"
    - "sparse subspace clustering algorithm theory and applications"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-2 (induced by source paper "algebraic clustering of affine subspaces"):
  with members:
    - "filtrated spectral algebraic subspace clustering"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "filtrated algebraic subspace clustering"
    - "robust subspace clustering"
    - "robust and efficient subspace segmentation via least squares regression"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "sparse subspace clustering algorithm theory and applications"

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)