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: "masked signal decomposition using subspace representation and its applications"
  Abstract: Signal decomposition is a classical problem in signal processing, which aims to separate an observed signal into two or more components each with its own property. Usually each component is described by its own subspace or dictionary. Extensive research has been done for the case where the components are additive, but in real world applications, the components are often non-additive. For example, an image may consist of a foreground object overlaid on a background, where each pixel either belongs to the foreground or the background. In such a situation, to separate signal components, we need to find a binary mask which shows the location of each component. Therefore it requires to solve a binary optimization problem. Since most of the binary optimization problems are intractable, we relax this problem to the approximated continuous problem, and solve it by alternating optimization technique, which solves the mask and the subspace projection for each component, alternatingly and iteratively. We show the application of the proposed algorithm for three applications: separation of text from background in images, separation of moving objects from a background undergoing global camera motion in videos, separation of sinusoidal and spike components in one dimensional signals. We demonstrate in each case that considering the non-additive nature of the problem can lead to significant improvement.

Connected through hyperedge HE-1 (induced by source paper "compressed sensing with probability based prior information"):
  with members:
    - "subspace orbit randomized decomposition for low rank matrix approximations"
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "on collaborative compressive sensing systems the framework design and algorithm"
    - "widely linear precoding for large scale mimo with iqi algorithms and performance analysis"
    - "joint sensing matrix and sparsifying dictionary optimization for tensor compressive sensing"
    - "iterative detection and decoding algorithms for mimo systems in block fading channels using ldpc codes"
    - "robust adaptive beamforming based on low rank and cross correlation techniques"
    - "a survey of sparse representation algorithms and applications"

Connected through hyperedge HE-2 (induced by source paper "masked rpca sparse and low rank decomposition under overlaying model and application to moving object detection"):
  with members:
    - "moving object detection by detecting contiguous outliers in the low rank representation"
    - "tilt transform invariant low rank textures"
    - "matrix completion from noisy entries"
    - "robust principal component analysis"

Connected through hyperedge HE-3 (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"
    - "an experimental study of deep convolutional features for iris recognition"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "screen content image segmentation using robust regression and sparse decomposition"

Connected through hyperedge HE-4 (induced by source paper "image segmentation using subspace representation and sparse decomposition"):
  with members:
    - "an experimental study of deep convolutional features for iris recognition"
    - "screen content image segmentation using robust regression and sparse decomposition"
    - "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-5 (induced by source paper "text extraction from texture images using masked signal decomposition"):
  with members:
    - "screen content image segmentation using robust regression and sparse decomposition"
    - "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-6 (induced by source paper "face recognition using scattering convolutional network"):
  with members:
    - "person re identification using visual attention"
    - "screen content image segmentation using robust regression and sparse decomposition"
    - "palmprint recognition using deep scattering convolutional network"
    - "medical image classification via svm using lbp features from saliency based folded data"
    - "iris recognition using scattering transform and textural features"
    - "fingerprint recognition using translation invariant scattering network"
    - "a bayesian framework for sparse representation based 3 d human pose estimation"
    - "highly accurate multispectral palmprint recognition using statistical and wavelet features"

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)