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: "image segmentation in video sequences a probabilistic approach"
  Abstract: Background subtraction is an old technique for finding moving objects in a video sequence for example, cars driving on a freeway. The idea is that subtracting the current image from a timeaveraged background image will leave only nonstationary objects. It is, however, a crude approximation to the task of classifying each pixel of the current image; it fails with slow-moving objects and does not distinguish shadows from moving objects. The basic idea of this paper is that we can classify each pixel using a model of how that pixel looks when it is part of different classes. We learn a mixture-of-Gaussians classification model for each pixel using an unsupervised technique- an efficient, incremental version of EM. Unlike the standard image-averaging approach, this automatically updates the mixture component for each class according to likelihood of membership; hence slow-moving objects are handled perfectly. Our approach also identifies and eliminates shadows much more effectively than other techniques such as thresholding. Application of this method as part of the Roadwatch traffic surveillance project is expected to result in significant improvements in vehicle identification and tracking.

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 "a foreground background parallel compression with residual encoding for surveillance video"):
  with members:
    - "enhancing quality for hevc compressed videos"
    - "generative adversarial networks for extreme learned image compression"
    - "stochastic adversarial video prediction"
    - "progressive spatial recurrent neural network for intra prediction"
    - "multi frame quality enhancement for compressed video"
    - "video compression through image interpolation"
    - "learning to generate time lapse videos using multi stage dynamic generative adversarial networks"

Connected through hyperedge HE-3 (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"
    - "towards benchmarking scene background initialization"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "support predicted modified cs for recursive robust principal components pursuit"

Connected through hyperedge HE-4 (induced by source paper "machine vision for natural gas methane emissions detection using an infrared camera"):
  with members:
    - "industrial smoke detection and visualization"
    - "an overview of gradient descent optimization algorithms"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "multi column deep neural networks for image classification"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-5 (induced by source paper "unsupervised video object segmentation for deep reinforcement learning"):
  with members:
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "unsupervised learning of depth and ego motion from video"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"
    - "sfm net learning of structure and motion from video"
    - "proximal policy optimization algorithms"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "asynchronous methods for deep reinforcement learning"
    - "learning to navigate in complex environments"

Connected through hyperedge HE-6 (induced by source paper "deep segmentation and registration in x ray angiography video"):
  with members:
    - "deepcut object segmentation from bounding box annotations using convolutional neural networks"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-7 (induced by source paper "video processing from electro optical sensors for object detection and tracking in maritime environment a survey"):
  with members:
    - "complex background subtraction by pursuing dynamic spatio temporal models"
    - "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 "moving object detection by detecting contiguous outliers in the low rank representation"):
  with members:
    - "stable principal component pursuit"
    - "network flow algorithms for structured sparsity"
    - "robust principal component analysis"

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)