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: "foreground segmentation using convolutional neural networks for multiscale feature encoding"
  Abstract: Abstract   Several methods have been proposed to solve moving objects segmentation problem accurately in different scenes. However, many of them lack the ability of handling various difficult scenarios such as illumination changes, background or camera motion, camouflage effect, shadow etc. To address these issues, we propose two robust encoder-decoder type neural networks that generate multi-scale feature encodings in different ways and can be trained end-to-end using only a few training samples. Using the same encoder-decoder configurations, in the first model, a triplet of encoders take the inputs in three scales to embed an image in a multi-scale feature space; in the second model, a Feature Pooling Module (FPM) is plugged on top of a single input encoder to extract multi-scale features in the middle layers. Both models use a transposed convolutional network in the decoder part to learn a mapping from feature space to image space. In order to evaluate our models, we entered the Change Detection 2014 Challenge (changedetection.net) and our models, namely FgSegNet_M and FgSegNet_S, outperformed all the existing state-of-the-art methods by an average F-Measure of 0.9770 and 0.9804, respectively. We also evaluate our models on SBI2015 and UCSD Background Subtraction datasets. Our source code is made publicly available at  https://github.com/lim-anggun/FgSegNet .

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"
    - "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"
    - "deep neural network concepts for background subtraction a systematic review and comparative evaluation"
    - "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 "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"
    - "deep neural network concepts for background subtraction a systematic review and comparative evaluation"
    - "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-4 (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"
    - "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-5 (induced by source paper "real time semantic background subtraction"):
  with members:
    - "image segmentation using deep learning a survey"
    - "background subtraction in real applications challenges current models and future directions"
    - "pyramid scene parsing network"

Connected through hyperedge HE-6 (induced by source paper "learning multi scale features for foreground segmentation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "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"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-7 (induced by source paper "combining background subtraction algorithms with convolutional neural network"):
  with members:
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "holistically nested edge detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "learning deconvolution network for semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "an integrated image filter for enhancing change detection results"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "efficient video object segmentation via network modulation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "conditional random fields as recurrent neural networks"
    - "efficient inference in fully connected crfs with gaussian edge potentials"
    - "moving object detection by detecting contiguous outliers in the low rank representation"

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)