human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "a robust regression approach for background foreground segmentation"
  Abstract: Background/foreground segmentation has a lot of applications in image and video processing. In this paper, a segmentation algorithm is proposed which is mainly designed for text and line extraction in screen content. The proposed method makes use of the fact that the background in each block is usually smoothly varying and can be modeled well by a linear combination of a few smoothly varying basis functions, while the foreground text and graphics create sharp discontinuity. The algorithm separates the background and foreground pixels by trying to fit pixel values in the block into a smooth function using a robust regression method. The inlier pixels that can fit well will be considered as background, while remaining outlier pixels will be considered foreground. This algorithm has been extensively tested on several images from HEVC standard test sequences for screen content coding, and is shown to have superior performance over other methods, such as the k-means clustering based segmentation algorithm in DjVu. This background/foreground segmentation can be used in different applications such as: text extraction, separate coding of background and foreground for compression of screen content and mixed content documents, principle line extraction from palmprint and crease detection in fingerprint images.

Connected through hyperedge HE-1 (induced by source paper "screen content image segmentation using sparse smooth decomposition"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "iris recognition using scattering transform and textural features"
    - "a subspace learning approach to high dimensional matrix decomposition with efficient information sampling"
    - "fingerprint recognition using translation invariant scattering network"
    - "screen content image segmentation using least absolute deviation fitting"
    - "multispectral palmprint recognition using textural features"

Connected through hyperedge HE-2 (induced by source paper "iris recognition using scattering transform and textural features"):
  with members:
    - "screen content image segmentation using least absolute deviation fitting"
    - "multispectral palmprint recognition using textural features"
    - "highly accurate multispectral palmprint recognition using statistical and wavelet features"
    - "invariant scattering convolution networks"
    - "classification with scattering operators"

Connected through hyperedge HE-3 (induced by source paper "screen content image segmentation using least absolute deviation fitting"):
  with members:
    - "multispectral palmprint recognition using textural features"
    - "multispectral palmprint recognition using a hybrid feature"
    - "a geometric approach for fully automatic chromosome segmentation"

Question: Please tell me which class the center node 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)