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 "subspace clustering using a symmetric low rank representation"):
  Abstract: In this paper, we propose a low-rank representation with symmetric constraint (LRRSC) method for robust subspace clustering. Given a collection of data points approximately drawn from multiple subspaces, the proposed technique can simultaneously recover the dimension and members of each subspace. LRRSC extends the original low-rank representation algorithm by integrating a symmetric constraint into the low-rankness property of high-dimensional data representation. The symmetric low-rank representation, which preserves the subspace structures of high-dimensional data, guarantees weight consistency for each pair of data points so that highly correlated data points of subspaces are represented together. Moreover, it can be efficiently calculated by solving a convex optimization problem. We provide a proof for minimizing the nuclear-norm regularized least square problem with a symmetric constraint. The affinity matrix for spectral clustering can be obtained by further exploiting the angular information of the principal directions of the symmetric low-rank representation. This is a critical step towards evaluating the memberships between data points. Besides, we also develop eLRRSC algorithm to improve the scalability of the original LRRSC by considering its closed form solution. Experimental results on benchmark databases demonstrate the effectiveness and robustness of LRRSC and its variant compared with several state-of-the-art subspace clustering algorithms.

Includes members:
  Member "automatic subspace learning via principal coefficients embedding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust subspace clustering with compressed data")
  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 "a unified framework for representation based subspace clustering of out of sample and large scale data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust subspace clustering by cauchy loss function")
  Member "symmetric low rank representation for subspace clustering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "research on clustering performance of sparse subspace clustering")
  Member "fast low rank representation based spatial pyramid matching for image classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "constrained low rank learning using least squares based regularization")
  Member "correntropy induced l2 graph for robust subspace clustering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "three stage subspace clustering framework with graph based transformation and optimization")
  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)