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: "deep neural network concepts for background subtraction a systematic review and comparative evaluation"
  Abstract: Conventional neural networks show a powerful framework for background subtraction in video acquired by static cameras. Indeed, the well-known SOBS method and its variants based on neural networks were the leader methods on the largescale CDnet 2012 dataset during a long time. Recently, convolutional neural networks which belong to deep learning methods were employed with success for background initialization, foreground detection and deep learned features. Currently, the top current background subtraction methods in CDnet 2014 are based on deep neural networks with a large gap of performance in comparison on the conventional unsupervised approaches based on multi-features or multi-cues strategies. Furthermore, a huge amount of papers was published since 2016 when Braham and Van Droogenbroeck published their first work on CNN applied to background subtraction providing a regular gain of performance. In this context, we provide the first review of deep neural network concepts in background subtraction for novices and experts in order to analyze this success and to provide further directions. For this, we first surveyed the methods used background initialization, background subtraction and deep learned features. Then, we discuss the adequacy of deep neural networks for background subtraction. Finally, experimental results are presented on the CDnet 2014 dataset.

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"
    - "foreground segmentation using convolutional neural networks for multiscale feature encoding"
    - "are object detection assessment criteria ready for maritime computer vision"
    - "an efficient optical flow based motion detection method for non stationary scenes"

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"
    - "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"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-3 (induced by source paper "self supervised object in gripper segmentation from robotic motions"):
  with members:
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "towards segmenting everything that moves"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "photorealistic image synthesis for object instance detection"
    - "learning instance segmentation by interaction"
    - "deep visual domain adaptation a survey"
    - "object discovery in videos as foreground motion clustering"
    - "fast online object tracking and segmentation a unifying approach"

Connected through hyperedge HE-4 (induced by source paper "bsuv net a fully convolutional neural network for background subtraction of unseen videos"):
  with members:
    - "combining background subtraction algorithms with convolutional neural network"
    - "3dfr a swift 3d feature reductionist framework for scene independent change detection"
    - "foreground segmentation using convolutional neural networks for multiscale feature encoding"
    - "background subtraction with real time semantic segmentation"
    - "how far can you get by combining change detection algorithms"
    - "rethinking atrous convolution for semantic image segmentation"
    - "pyramid scene parsing network"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (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"
    - "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"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-6 (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"
    - "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"
    - "robust online matrix factorization for dynamic 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-7 (induced by source paper "illumination invariant foreground object segmentation using foregans"):
  with members:
    - "unsupervised rgbd video object segmentation using gans"
    - "semantic image inpainting with deep generative models"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "moving object detection by detecting contiguous outliers in the low rank representation"

Connected through hyperedge HE-8 (induced by source paper "background subtraction with real time semantic segmentation"):
  with members:
    - "foreground segmentation using convolutional neural networks for multiscale feature encoding"
    - "how far can you get by combining change detection algorithms"
    - "linknet exploiting encoder representations for efficient semantic segmentation"
    - "icnet for real time semantic segmentation on high resolution images"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "parallel tracking and verifying a framework for real time and high accuracy visual tracking"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

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)