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: "screen content image segmentation using robust regression and sparse decomposition"
  Abstract: This paper considers how to separate text and/or graphics from smooth background in screen content and mixed document images and proposes two approaches to perform this segmentation task. The proposed methods make 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 algorithms separate the background and foreground pixels by trying to fit background pixel values in the block into a smooth function using two different schemes. One is based on robust regression, where the inlier pixels will be considered as background, while remaining outlier pixels will be considered foreground. The second approach uses a sparse decomposition framework where the background and foreground layers are modeled with a smooth and sparse components respectively. These algorithms have been tested on images extracted from HEVC standard test sequences for screen content coding, and are shown to have superior performance over previous approaches. The proposed methods can be used in different applications such as text extraction, separate coding of background and foreground for compression of screen content, and medical image segmentation.

Connected through hyperedge HE-1 (induced by source paper "finger gan generating realistic fingerprint images using connectivity imposed gan"):
  with members:
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "multicolumn networks for face recognition"
    - "mtbi identification from diffusion mr images using bag of adversarial visual features"
    - "iris gan learning to generate realistic iris images using convolutional gan"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "masked signal decomposition using subspace representation and its applications"
    - "an experimental study of deep convolutional features for iris recognition"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "image segmentation using subspace representation and sparse decomposition"):
  with members:
    - "masked signal decomposition using subspace representation and its applications"
    - "an experimental study of deep convolutional features for iris recognition"
    - "robust computation of linear models by convex relaxation"
    - "learning deconvolution network for semantic segmentation"
    - "hyperspectral and multispectral image fusion based on a sparse representation"
    - "fully convolutional networks for semantic segmentation"
    - "stable image reconstruction using total variation minimization"
    - "tilt transform invariant low rank textures"

Connected through hyperedge HE-3 (induced by source paper "text extraction from texture images using masked signal decomposition"):
  with members:
    - "masked signal decomposition using subspace representation and its applications"
    - "text flow a unified text detection system in natural scene images"
    - "textproposals a text specific selective search algorithm for word spotting in the wild"
    - "screen content image segmentation using sparse decomposition and total variation minimization"
    - "iris recognition using scattering transform and textural features"
    - "fingerprint recognition using translation invariant scattering network"
    - "a hough transform based technique for text segmentation"

Connected through hyperedge HE-4 (induced by source paper "subspace learning in the presence of sparse structured outliers and noise"):
  with members:
    - "screen content image segmentation using sparse decomposition and total variation minimization"
    - "screen content image segmentation using sparse smooth decomposition"
    - "robust computation of linear models by convex relaxation"
    - "screen content image segmentation using least absolute deviation fitting"
    - "optimization with sparsity inducing penalties"

Connected through hyperedge HE-5 (induced by source paper "multi view task driven recognition in visual sensor networks"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "cloud based deep learning of big eeg data for epileptic seizure prediction"
    - "control allocation for wide area coordinated damping"
    - "feature encoding in band limited distributed surveillance systems"
    - "quality based multimodal classification using tree structured sparsity"

Connected through hyperedge HE-6 (induced by source paper "masked signal decomposition using subspace representation and its applications"):
  with members:
    - "screen content image segmentation using least absolute deviation fitting"
    - "tilt transform invariant low rank textures"
    - "robust principal component analysis"

Connected through hyperedge HE-7 (induced by source paper "superpixel based segmentation and classification of polyps in wireless capsule endoscopy"):
  with members:
    - "palmprint recognition using deep scattering convolutional network"

Connected through hyperedge HE-8 (induced by source paper "robust and scalable column row sampling from corrupted big data"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "spatial random sampling a structure preserving data sketching tool"
    - "innovation pursuit a new approach to subspace clustering"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "screen content image segmentation using sparse decomposition and total variation minimization"
    - "a subspace learning approach to high dimensional matrix decomposition with efficient information sampling"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "dissimilarity based sparse subset selection"

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)