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 "algebraic clustering of affine subspaces"):
  Abstract: Subspace clustering is an important problem in machine learning with many applications in computer vision and pattern recognition. Prior work has studied this problem using algebraic, iterative, statistical, low-rank and sparse representation techniques. While these methods have been applied to both linear and affine subspaces, theoretical results have only been established in the case of linear subspaces. For example, algebraic subspace clustering (ASC) is guaranteed to provide the correct clustering when the data points are in general position and the union of subspaces is transversal. In this paper we study in a rigorous fashion the properties of ASC in the case of affine subspaces. Using notions from algebraic geometry, we prove that the homogenization trick, which embeds points in a union of affine subspaces into points in a union of linear subspaces, preserves the general position of the points and the transversality of the union of subspaces in the embedded space, thus establishing the correctness of ASC for affine subpaces.

Includes members:
  Member "sparse subspace clustering by orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scalable sparse subspace clustering by orthogonal matching pursuit")
  Member "filtrated spectral algebraic subspace clustering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cur decompositions similarity matrices and subspace clustering")
  Member "scalable sparse subspace clustering by orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi level representation learning for deep subspace clustering")
  Member "filtrated algebraic subspace clustering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an algebraic geometric approach for linear regression without correspondences")
  Member "robust subspace clustering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "provably efficient exploration for rl with unsupervised learning")
  Member "robust and efficient subspace segmentation via least squares regression":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning mixtures of linear regressions in subexponential time via fourier moments")
  Member "robust recovery of subspace structures by low rank representation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gated fusion network for degraded image super resolution")
  Member "generalized principal component analysis gpca":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "salient object detection a survey")

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)