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 "restricted connection orthogonal matching pursuit for sparse subspace clustering"):
  Abstract: Sparse Subspace Clustering (SSC) is one of the most popular methods for clustering data points into their underlying subspaces. However, SSC may suffer from heavy computational burden. Orthogonal Matching Pursuit applied on SSC accelerates the computation but the trade-off is the loss of clustering accuracy. In this paper, we propose a noise-robust algorithm, Restricted Connection Orthogonal Matching Pursuit for Sparse Subspace Clustering (RCOMP-SSC), to improve the clustering accuracy and maintain the low computational time by restricting the number of connections of each data point during the iteration of OMP. Also, we develop a framework of control matrix to realize RCOMP-SCC. And the framework is scalable for other data point selection strategies. Our analysis and experiments on synthetic data and two real-world databases (EYaleB & Usps) demonstrate the superiority of our algorithm compared with other clustering methods in terms of accuracy and computational time.

Includes members:
  Member "robust subspace clustering by cauchy loss function":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neither global nor local a hierarchical robust subspace clustering for image data")
  Member "active orthogonal matching pursuit for sparse subspace clustering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "theory of spectral method for union of subspaces based random geometry graph")
  Member "noisy subspace clustering via matching pursuits":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "basis pursuit and orthogonal matching pursuit for subspace preserving recovery theoretical analysis")
  Member "structured sparse subspace clustering a joint affinity learning and subspace clustering framework":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "self expressive subspace clustering to recognize motion dynamics of a multi joint coordination for chronic ankle instability")
  Member "oracle based active set algorithm for scalable elastic net subspace clustering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi level representation learning for deep subspace clustering")
  Member "scalable sparse subspace clustering by orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi level representation learning for deep subspace clustering")
  Member "algebraic clustering of affine subspaces":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scaled simplex representation for subspace clustering")
  Member "robust and efficient subspace segmentation via least squares regression":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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.LG(Machine Learning)