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 "screen content image segmentation using sparse smooth decomposition"):
  Abstract: Sparse decomposition has been extensively used for different applications including signal compression and denoising and document analysis. In this paper, sparse decomposition is used for image segmentation. The proposed algorithm separates the background and foreground using a sparse-smooth decomposition technique such that the smooth and sparse components correspond to the background and foreground respectively. This algorithm is tested on several test images from HEVC test sequences and is shown to have superior performance over other methods, such as the hierarchical k-means clustering in DjVu. This segmentation algorithm can also be used for text extraction, video compression and medical image segmentation.

Includes members:
  Member "high dimensional low rank plus sparse matrix decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "evolutionary self expressive models for subspace clustering")
  Member "iris recognition using scattering transform and textural features":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deepiris iris recognition using a deep learning approach")
  Member "a subspace learning approach to high dimensional matrix decomposition with efficient information sampling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "coherence pursuit fast simple and robust principal component analysis")
  Member "fingerprint recognition using translation invariant scattering network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a study on wrist identification for forensic investigation")
  Member "screen content image segmentation using least absolute deviation fitting":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "enhanced signal recovery via sparsity inducing image priors")
  Member "multispectral palmprint recognition using textural features":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "iris gan learning to generate realistic iris images using convolutional gan")
  Member "a robust regression approach for background foreground segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "iris recognition using scattering transform and textural features")

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)