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 "robust subspace clustering via tighter rank approximation"):
  Abstract: Matrix rank minimization problem is in general NP-hard. The nuclear norm is used to substitute the rank function in many recent studies. Nevertheless, the nuclear norm approximation adds all singular values together and the approximation error may depend heavily on the magnitudes of singular values. This might restrict its capability in dealing with many practical problems. In this paper, an arctangent function is used as a tighter approximation to the rank function. We use it on the challenging subspace clustering problem. For this nonconvex minimization problem, we develop an effective optimization procedure based on a type of augmented Lagrange multipliers (ALM) method. Extensive experiments on face clustering and motion segmentation show that the proposed method is effective for rank approximation.

Includes members:
  Member "logdet rank minimization with application to subspace clustering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fast low rank bayesian matrix completion with hierarchical gaussian prior models")
  Member "generalized nonconvex nonsmooth low rank minimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ell_0 motivated low rank sparse subspace clustering")
  Member "robust recovery of subspace structures by low rank representation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gated fusion network for degraded image super resolution")
  Member "efficient sparse group feature selection via nonconvex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust pca via nonconvex rank approximation")
  Member "sparse subspace clustering algorithm theory and applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "provably efficient exploration for rl with unsupervised learning")
  Member "a geometric analysis of subspace clustering with outliers":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "arae adversarially robust training of autoencoders improves novelty detection")
  Member "exact subspace segmentation and outlier detection by low rank representation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robust subspace clustering with compressed data")
  Member "the power of convex relaxation near optimal matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ell_0 motivated low rank sparse subspace clustering")

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)