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: "robust online matrix factorization for dynamic background subtraction"
  Abstract: We propose an effective online background subtraction method, which can be robustly applied to practical videos that have variations in both foreground and background. Different from previous methods which often model the foreground as Gaussian or Laplacian distributions, we model the foreground for each frame with a specific mixture of Gaussians (MoG) distribution, which is updated online frame by frame. Particularly, our MoG model in each frame is regularized by the learned foreground/background knowledge in previous frames. This makes our online MoG model highly robust, stable and adaptive to practical foreground and background variations. The proposed model can be formulated as a concise probabilistic MAP model, which can be readily solved by EM algorithm. We further embed an affine transformation operator into the proposed model, which can be automatically adjusted to fit a wide range of video background transformations and make the method more robust to camera movements. With using the sub-sampling technique, the proposed method can be accelerated to execute more than 250 frames per second on average, meeting the requirement of real-time background subtraction for practical video processing tasks. The superiority of the proposed method is substantiated by extensive experiments implemented on synthetic and real videos, as compared with state-of-the-art online and offline background subtraction methods.

Connected through hyperedge HE-1 (induced by source paper "a robust visual system for small target motion detection against cluttered moving backgrounds"):
  with members:
    - "a directionally selective small target motion detecting visual neural network in cluttered backgrounds"
    - "an improved lptc neural model for background motion direction estimation"

Connected through hyperedge HE-2 (induced by source paper "toward real world single image super resolution a new benchmark and a new model"):
  with members:
    - "deep learning for single image super resolution a brief review"
    - "zoom to learn learn to zoom"
    - "camera lens super resolution"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "toward bridging the simulated to real gap benchmarking super resolution on real data"
    - "residual dense network for image super resolution"

Connected through hyperedge HE-3 (induced by source paper "variational denoising network toward blind noise modeling and removal"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "toward convolutional blind denoising of real photographs"
    - "neural nearest neighbors networks"
    - "non local recurrent network for image restoration"
    - "renoir a dataset for real low light image noise reduction"
    - "residual dense network for image super resolution"
    - "unprocessing images for learned raw denoising"

Connected through hyperedge HE-4 (induced by source paper "deep autoencoders with value at risk thresholding for unsupervised anomaly detection"):
  with members:
    - "background subtraction in real applications challenges current models and future directions"
    - "learning multi scale features for foreground segmentation"
    - "deep neural network concepts for background subtraction a systematic review and comparative evaluation"
    - "an overview of robust subspace recovery"
    - "robust subspace learning robust pca robust subspace tracking and robust subspace recovery"
    - "a deep convolutional neural network for background subtraction"
    - "fast optical flow using dense inverse search"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"

Connected through hyperedge HE-5 (induced by source paper "video rain snow removal by transformed online multiscale convolutional sparse coding"):
  with members:
    - "robust video content alignment and compensation for clear vision through the rain"
    - "desnownet context aware deep network for snow removal"
    - "clearing the skies a deep network architecture for single image rain removal"
    - "image de raining using a conditional generative adversarial network"
    - "deep joint rain detection and removal from a single image"
    - "robust low rank matrix factorization under general mixture noise distributions"
    - "multi scale context aggregation by dilated convolutions"
    - "multi scale orderless pooling of deep convolutional activation features"

Connected through hyperedge HE-6 (induced by source paper "joint label prediction based semi supervised adaptive concept factorization for robust data representation"):
  with members:
    - "support vector algorithms for optimizing the partial area under the roc curve"
    - "a deep matrix factorization method for learning attribute representations"

Connected through hyperedge HE-7 (induced by source paper "robust video content alignment and compensation for clear vision through the rain"):
  with members:
    - "robust video content alignment and compensation for rain removal in a cnn framework"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep joint rain detection and removal from a single image"
    - "accurate image super resolution using very deep convolutional networks"
    - "adam a method for stochastic optimization"
    - "matconvnet convolutional neural networks for matlab"
    - "robust principal component analysis"

Connected through hyperedge HE-8 (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"
    - "fast algorithms for robust pca via gradient descent"
    - "correlated pca principal components analysis when data and noise are correlated"
    - "nearly optimal robust matrix completion"
    - "online matrix completion and online robust pca"

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)