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: "a deep convolutional neural network for background subtraction"
  Abstract: In this work, we present a novel background subtraction system that uses a deep Convolutional Neural Network (CNN) to perform the segmentation. With this approach, feature engineering and parameter tuning become unnecessary since the network parameters can be learned from data by training a single CNN that can handle various video scenes. Additionally, we propose a new approach to estimate background model from video. For the training of the CNN, we employed randomly 5 percent video frames and their ground truth segmentations taken from the Change Detection challenge 2014(CDnet 2014). We also utilized spatial-median filtering as the post-processing of the network outputs. Our method is evaluated with different data-sets, and the network outperforms the existing algorithms with respect to the average ranking over different evaluation metrics. Furthermore, due to the network architecture, our CNN is capable of real time processing.

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 "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"
    - "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"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (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"
    - "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-4 (induced by source paper "the history began from alexnet a comprehensive survey on deep learning approaches"):
  with members:
    - "hyperspectral image classification with markov random fields and a convolutional neural network"
    - "an analysis of deep neural network models for practical applications"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "deep reinforcement learning an overview"

Connected through hyperedge HE-5 (induced by source paper "recognition in terra incognita"):
  with members:
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "domain adaptation for visual applications a comprehensive survey"
    - "the devil is in the tails fine grained classification in the wild"
    - "road reality oriented adaptation for semantic segmentation of urban scenes"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"
    - "automatically identifying wild animals in camera trap images with deep learning"
    - "automatically identifying counting and describing wild animals in camera trap images with deep learning"

Connected through hyperedge HE-6 (induced by source paper "a foreground inference network for video surveillance using multi view receptive field"):
  with members:
    - "how far can you get by combining change detection algorithms"
    - "instance sensitive fully convolutional networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "understanding convolutional neural networks with a mathematical model"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "xception deep learning with depthwise separable convolutions"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (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"
    - "show attend and tell neural image caption generation with visual attention"
    - "deep residual learning for image recognition"
    - "towards benchmarking scene background initialization"
    - "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-8 (induced by source paper "online adaptation of convolutional neural networks for video object segmentation"):
  with members:
    - "super trajectory for video segmentation"
    - "full resolution residual networks for semantic segmentation in street scenes"
    - "bridging category level and instance level semantic image segmentation"
    - "learning motion patterns in videos"
    - "video propagation networks"
    - "wider or deeper revisiting the resnet model for visual recognition"
    - "learning to track at 100 fps with deep regression networks"
    - "deeptrack learning discriminative feature representations online for robust visual tracking"

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)