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: "efficient solvers for sparse subspace clustering"
  Abstract: Sparse subspace clustering (SSC) is a popular method in machine learning and computer vision for clustering $n$ data points that lie near a union of low-dimensional linear or affine subspaces. The standard models introduced by Elhamifar and Vidal express each data point as a sparse linear or affine combination of the other data points, using either $\ell_1$ or $\ell_0$ regularization to enforce sparsity. The $\ell_1$ model, which is convex and has theoretical guarantees but requires $O(n^2)$ storage, is typically solved by the alternating direction method of multipliers (ADMM) which takes $O(n^3)$ flops. The $\ell_0$ model, which is preferred for large $n$ since it only needs memory linear in $n$, is typically solved via orthogonal matching pursuit (OMP) and cannot handle the case of affine subspaces. Our first contribution is to show how ADMM can be modified using the matrix-inversion lemma to take $O(n^2)$ flops instead of $O(n^3)$. Then, our main contribution is to show that a proximal gradient framework can solve SSC, covering both $\ell_1$ and $\ell_0$ models, and both linear and affine constraints. For both $\ell_1$ and $\ell_0$, the proximity operator with affine constraints is non-trivial, so we derive efficient proximity operators. In the $\ell_1$ case, our method takes just $O(n^2)$ flops, while in the $\ell_0$ case, the memory is linear in $n$. This is the first algorithm to solve the $\ell_0$ problem in conjunction with affine constraints. Numerical experiments on synthetic and real data demonstrate that the proximal gradient based solvers are state-of-the-art, but more importantly, we argue that they are more convenient to use than ADMM-based solvers because ADMM solvers are highly sensitive to a solver parameter that may be data set dependent.

Connected through hyperedge HE-1 (induced by source paper "large scale multi view subspace clustering in linear time"):
  with members:
    - "robust graph learning from noisy data"
    - "multi view multiple clustering"
    - "spectral clustering via ensemble deep autoencoder learning sc edae"
    - "multiple partitions aligned clustering"
    - "clustering with similarity preserving"
    - "multi view low rank sparse subspace clustering"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"

Connected through hyperedge HE-2 (induced by source paper "large scale sparse subspace clustering using landmarks"):
  with members:
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "robust subspace clustering"
    - "sparse modeling for image and vision processing"
    - "sparse subspace clustering algorithm theory and applications"
    - "invariant scattering convolution networks"
    - "a geometric analysis of subspace clustering with outliers"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "greedy frank wolfe algorithm for exemplar selection"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "emnist an extension of mnist to handwritten letters"
    - "dimensionality reduction of massive sparse datasets using coresets"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "sparse subspace clustering algorithm theory and applications"
    - "an improved approximation algorithm for the column subset selection problem"
    - "relative error cur matrix decompositions"

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)