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: "background subtraction via generalized fused lasso foreground modeling"
  Abstract: Background Subtraction (BS) is one of the key steps in video analysis. Many background models have been proposed and achieved promising performance on public data sets. However, due to challenges such as illumination change, dynamic background etc. the resulted foreground segmentation often consists of holes as well as background noise. In this regard, we consider generalized fused lasso regularization to quest for intact structured foregrounds. Together with certain assumptions about the background, such as the low-rank assumption or the sparse-composition assumption (depending on whether pure background frames are provided), we formulate BS as a matrix decomposition problem using regularization terms for both the foreground and background matrices. Moreover, under the proposed formulation, the two generally distinctive background assumptions can be solved in a unified manner. The optimization was carried out via applying the augmented Lagrange multiplier (ALM) method in such a way that a fast parametric-flow algorithm is used for updating the foreground matrix. Experimental results on several popular BS data sets demonstrate the advantage of the proposed model compared to state-of-the-arts.

Connected through hyperedge HE-1 (induced by source paper "g lbm generative low dimensional background model estimation from video sequences"):
  with members:
    - "deeppbm deep probabilistic background model estimation from video sequences"
    - "background subtraction in real applications challenges current models and future directions"
    - "deep neural network concepts for background subtraction a systematic review and comparative evaluation"
    - "foreground segmentation using convolutional neural networks for multiscale feature encoding"
    - "a robust background initialization algorithm with superpixel motion detection"
    - "neural collaborative filtering"
    - "background subtraction via fast robust matrix completion"
    - "learning features by watching objects move"

Connected through hyperedge HE-2 (induced by source paper "error bounded foreground and background modeling for moving object detection in satellite videos"):
  with members:
    - "tracking in aerial hyperspectral videos using deep kernelized correlation filters"
    - "corola a sequential solution to moving object detection using low rank approximation"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "moving object detection by detecting contiguous outliers in the low rank representation"
    - "stable principal component pursuit"
    - "robust principal component analysis"

Connected through hyperedge HE-3 (induced by source paper "moving object detection under discontinuous change in illumination using tensor low rank and invariant sparse decomposition"):
  with members:
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "total variation regularized tensor rpca for background subtraction from compressive measurements"
    - "corola a sequential solution to moving object detection using low rank approximation"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "moving object detection by detecting contiguous outliers in the low rank representation"
    - "robust principal component analysis"

Connected through hyperedge HE-4 (induced by source paper "unsupervised rgbd video object segmentation using gans"):
  with members:
    - "unsupervised deep context prediction for background foreground separation"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "robust subspace learning robust pca robust subspace tracking and robust subspace recovery"
    - "semantic image inpainting with deep generative models"
    - "inverting visual representations with convolutional networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "understanding deep image representations by inverting them"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "weighted low rank approximation of matrices and background modeling"):
  with members:
    - "towards benchmarking scene background initialization"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "moving object detection by detecting contiguous outliers in the low rank representation"
    - "support predicted modified cs for recursive robust principal components pursuit"
    - "robust principal component analysis"

Connected through hyperedge HE-6 (induced by source paper "robust subspace learning robust pca robust subspace tracking and robust subspace recovery"):
  with members:
    - "provable dynamic robust pca or robust subspace tracking"
    - "an overview of robust subspace recovery"
    - "low rank phase retrieval"
    - "nearly optimal robust subspace tracking"
    - "robust online matrix factorization for dynamic background subtraction"
    - "fast algorithms for robust pca via gradient descent"
    - "correlated pca principal components analysis when data and noise are correlated"
    - "nearly optimal robust matrix completion"

Connected through hyperedge HE-7 (induced by source paper "cvpaper challenge in 2015 a review of cvpr2015 and deepsurvey"):
  with members:
    - "semi supervised learning with explicit relationship regularization"
    - "understanding image virality"
    - "watch and learn semi supervised learning of object detectors from videos"
    - "deep carving discovering visual attributes by carving deep neural nets"
    - "improving object detection with deep convolutional networks via bayesian optimization and structured prediction"
    - "bilinear random projections for locality sensitive binary codes"
    - "learning multiple visual tasks while discovering their structure"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-8 (induced by source paper "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "can dmd obtain a scene background in color"
    - "efficient background modeling based on sparse representation and outlier iterative removal"
    - "fast algorithms for robust pca via gradient descent"
    - "fast robust pca on graphs"
    - "approximated robust principal component analysis for improved general scene background subtraction"
    - "mpi faun an mpi based framework for alternating updating nonnegative matrix factorization"

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)