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 "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.

Includes members:
  Member "approximating spectral clustering via sampling a review":
    also connected through other hyperedges:
      (none)
  Member "large scale sparse subspace clustering using landmarks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the effectiveness of variational autoencoders for active learning")
  Member "active orthogonal matching pursuit for sparse subspace clustering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "restricted connection orthogonal matching pursuit for sparse subspace clustering")
  Member "theoretical analysis of sparse subspace clustering with missing entries":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lstm assisted evolutionary self expressive subspace clustering")
  Member "scalable and robust sparse subspace clustering using randomized clustering and multilayer graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "exactly robust kernel principal component analysis")
  Member "unconstrained and curvature constrained shortest path distances and their approximation":
    also connected through other hyperedges:
      (none)
  Member "oracle based active set algorithm for scalable elastic net subspace clustering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi level representation learning for deep subspace clustering")
  Member "adaptive admm with spectral penalty parameter selection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "catching cheats detecting strategic manipulation in distributed optimisation of electric vehicle aggregators")

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)