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 "simplex representation for subspace clustering"):
  Abstract: Spectral clustering based methods have achieved leading performance on subspace clustering problem. State-of-the-art subspace clustering methods follow a three-stage framework: compute a coefficient matrix from the data by solving an optimization problem; construct an affinity matrix from the coefficient matrix; and obtain the final segmentation by applying spectral clustering to the affinity matrix. To construct a feasible affinity matrix, these methods mostly employ the operations of exponentiation, absolutely symmetrization, or squaring, etc. However, all these operations will force the negative entries (which cannot be explicitly avoided) in the coefficient matrix to be positive in computing the affinity matrix, and consequently damage the inherent correlations among the data. In this paper, we introduce the simplex representation (SR) to remedy this problem of representation based subspace clustering. We propose an SR based least square regression (SRLSR) model to construct a physically more meaningful affinity matrix by integrating the nonnegative property of graph into the representation coefficient computation while maintaining the discrimination of original data. The SRLSR model is reformulated as a linear equality-constrained problem, which is solved efficiently under the alternating direction method of multipliers framework. Experiments on benchmark datasets demonstrate that the proposed SRLSR algorithm is very efficient and outperforms state-of-the-art subspace clustering methods on accuracy.

Includes members:
  Member "deep subspace clustering networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gatcluster self supervised gaussian attention network for image clustering")
  Member "scalable sparse subspace clustering by orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi level representation learning for deep subspace clustering")
  Member "dissimilarity based sparse subset selection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a multi criteria approach for fast and outlier aware representative selection from manifolds")
  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 "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 "invariant scattering convolution networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spatially adaptive filter units for compact and efficient deep neural networks")

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)