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: "towards benchmarking scene background initialization"
  Abstract: Given a set of images of a scene taken at different times, the availability of an initial background model that describes the scene without foreground objects is the prerequisite for a wide range of applications, ranging from video surveillance to computational photography. Even though several methods have been proposed for scene background initialization, the lack of a common groundtruthed dataset and of a common set of metrics makes it difficult to compare their performance. To move first steps towards an easy and fair comparison of these methods, we assembled a dataset of sequences frequently adopted for background initialization, selected or created ground truths for quantitative evaluation through a selected suite of metrics, and compared results obtained by some existing methods, making all the material publicly available.

Connected through hyperedge HE-1 (induced by source paper "moving objects detection with a moving camera a comprehensive review"):
  with members:
    - "deeppbm deep probabilistic background model estimation from video sequences"
    - "the 2019 davis challenge on vos unsupervised multi object segmentation"
    - "combining background subtraction algorithms with convolutional neural network"
    - "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"
    - "foreground segmentation using convolutional neural networks for multiscale feature encoding"
    - "are object detection assessment criteria ready for maritime computer vision"

Connected through hyperedge HE-2 (induced by source paper "graphbgs background subtraction via recovery of graph signals"):
  with members:
    - "background subtraction in real applications challenges current models and future directions"
    - "a fully convolutional neural network for background subtraction of unseen videos"
    - "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"
    - "background subtraction with real time semantic segmentation"
    - "mask r cnn"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"

Connected through hyperedge HE-3 (induced by source paper "learning multi scale features for foreground segmentation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "foreground segmentation using convolutional neural networks for multiscale feature encoding"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "how far can you get by combining change detection algorithms"
    - "a deep convolutional neural network for background subtraction"
    - "rethinking atrous convolution for semantic image segmentation"
    - "instance normalization the missing ingredient for fast stylization"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-4 (induced by source paper "background subtraction in real applications challenges current models and future directions"):
  with members:
    - "on deterministic sampling patterns for robust low rank matrix completion"
    - "a comparison between background modelling methods for vehicle segmentation in highway traffic videos"
    - "recognition in terra incognita"
    - "robust subspace learning robust pca robust subspace tracking and robust subspace recovery"
    - "on the role and the importance of features for background modeling and foreground detection"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "image segmentation in video sequences a probabilistic approach"
    - "support predicted modified cs for recursive robust principal components pursuit"

Connected through hyperedge HE-5 (induced by source paper "foreground segmentation using convolutional neural networks for multiscale feature encoding"):
  with members:
    - "how far can you get by combining change detection algorithms"
    - "a deep convolutional neural network for background subtraction"
    - "show attend and tell neural image caption generation with visual attention"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "efficient object localization using convolutional networks"
    - "translating videos to natural language using deep recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "weighted low rank approximation of matrices and background modeling"):
  with members:
    - "background subtraction via generalized fused lasso foreground modeling"
    - "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-7 (induced by source paper "unsupervised deep context prediction for background foreground separation"):
  with members:
    - "high resolution image inpainting using multi scale neural patch synthesis"
    - "randomized low rank dynamic mode decomposition for motion detection"
    - "context encoders feature learning by inpainting"
    - "very deep convolutional networks for large scale image recognition"
    - "moving object detection by detecting contiguous outliers in the low rank representation"
    - "robust principal component analysis"

Connected through hyperedge HE-8 (induced by source paper "full reference objective quality assessment for reconstructed background images"):
  with members:
    - "a low complexity algorithm for static background estimation from cluttered image sequences in surveillance contexts"

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)