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 "scalable and robust sparse subspace clustering using randomized clustering and multilayer graphs"):
  Abstract: Sparse subspace clustering (SSC) is one of the current state-of-the-art method for partitioning data points into the union of subspaces, with strong theoretical guarantees. However, it is not practical for large data sets as it requires solving a LASSO problem for each data point, where the number of variables in each LASSO problem is the number of data points. To improve the scalability of SSC, we propose to select a few sets of anchor points using a randomized hierarchical clustering method, and, for each set of anchor points, solve the LASSO problems for each data point allowing only anchor points to have a non-zero weight (this reduces drastically the number of variables). This generates a multilayer graph where each layer corresponds to a different set of anchor points. Using the Grassmann manifold of orthogonal matrices, the shared connectivity among the layers is summarized within a single subspace. Finally, we use $k$-means clustering within that subspace to cluster the data points, similarly as done by spectral clustering in SSC. We show on both synthetic and real-world data sets that the proposed method not only allows SSC to scale to large-scale data sets, but that it is also much more robust as it performs significantly better on noisy data and on data with close susbspaces and outliers, while it is not prone to oversegmentation.

Includes members:
  Member "active orthogonal matching pursuit for sparse subspace clustering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "restricted connection orthogonal matching pursuit for sparse subspace clustering")
  Member "efficient sparse subspace clustering by nearest neighbour filtering":
    also connected through other hyperedges:
      (none)
  Member "oracle based active set algorithm for scalable elastic net subspace clustering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi level representation learning for deep subspace clustering")
  Member "hierarchical clustering of hyperspectral images using rank two nonnegative matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalized separable nonnegative matrix factorization")
  Member "scalable sparse subspace clustering by orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi level representation learning for deep subspace clustering")
  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 "provable deterministic leverage score sampling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improved subsampled randomized hadamard transform for linear svm")
  Member "robust and efficient subspace segmentation via least squares regression":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning mixtures of linear regressions in subexponential time via fourier moments")

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)