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 "symmetric low rank representation for subspace clustering"):
  Abstract: We propose a symmetric low-rank representation (SLRR) method for subspace clustering, which assumes that a data set is approximately drawn from the union of multiple subspaces. The proposed technique can reveal the membership of multiple subspaces through the self-expressiveness property of the data. In particular, the SLRR method considers a collaborative representation combined with low-rank matrix recovery techniques as a low-rank representation to learn a symmetric low-rank representation, which preserves the subspace structures of high-dimensional data. In contrast to performing iterative singular value decomposition in some existing low-rank representation based algorithms, the symmetric low-rank representation in the SLRR method can be calculated as a closed form solution by solving the symmetric low-rank optimization problem. By making use of the angular information of the principal directions of the symmetric low-rank representation, an affinity graph matrix is constructed for spectral clustering. Extensive experimental results show that it outperforms state-of-the-art subspace clustering algorithms. HighlightsA symmetric low-rank representation method for subspace clustering is proposed.A collaborative representation and dimension reduction are combined in learning.The symmetric low-rank representation can be calculated as a closed form solution.The symmetric low-rank representation maintains the subspace structures of data.The proposed method outperforms state-of-the-art subspace clustering algorithms.

Includes members:
  Member "subspace clustering using a symmetric low rank representation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "research on clustering performance of sparse subspace clustering")
  Member "constructing the l2 graph for robust subspace learning and subspace clustering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ell_0 motivated low rank sparse subspace clustering")
  Member "robust and efficient subspace segmentation via least squares regression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "gated fusion network for degraded image super resolution")
  Member "greedy feature selection for subspace clustering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi level representation learning for deep subspace clustering")
  Member "generalized principal component analysis gpca":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "salient object detection a survey")
  Member "fixed rank representation for unsupervised visual learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "k meansnet when k means meets differentiable programming")
  Member "sparse subspace clustering algorithm theory and applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "provably efficient exploration for rl with unsupervised learning")

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)