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: "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
  Abstract: Recent research on problem formulations based on decomposition into low-rank plus sparse matrices shows a suitable framework to separate moving objects from the background. The most representative problem formulation is the Robust Principal Component Analysis (RPCA) solved via Principal Component Pursuit (PCP) which decomposes a data matrix in a low-rank matrix and a sparse matrix. However, similar robust implicit or explicit decompositions can be made in the following problem formulations: Robust Non-negative Matrix Factorization (RNMF), Robust Matrix Completion (RMC), Robust Subspace Recovery (RSR), Robust Subspace Tracking (RST) and Robust Low-Rank Minimization (RLRM). The main goal of these similar problem formulations is to obtain explicitly or implicitly a decomposition into low-rank matrix plus additive matrices. In this context, this work aims to initiate a rigorous and comprehensive review of the similar problem formulations in robust subspace learning and tracking based on decomposition into low-rank plus additive matrices for testing and ranking existing algorithms for background/foreground separation. For this, we first provide a preliminary review of the recent developments in the different problem formulations which allows us to define a unified view that we called Decomposition into Low-rank plus Additive Matrices (DLAM). Then, we examine carefully each method in each robust subspace learning/tracking frameworks with their decomposition, their loss functions, their optimization problem and their solvers. Furthermore, we investigate if incremental algorithms and real-time implementations can be achieved for background/foreground separation. Finally, experimental results on a large-scale dataset called Background Models Challenge (BMC 2012) show the comparative performance of 32 different robust subspace learning/tracking methods.

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 "online structured sparsity based moving object detection from satellite videos"):
  with members:
    - "error bounded foreground and background modeling for moving object detection in satellite videos"
    - "dota a large scale dataset for object detection in aerial images"
    - "tracking in aerial hyperspectral videos using deep kernelized correlation filters"
    - "a survey on object detection in optical remote sensing images"
    - "ssd single shot multibox detector"
    - "you only look once unified real time object detection"
    - "fast r cnn"
    - "corola a sequential solution to moving object detection using low rank approximation"

Connected through hyperedge HE-4 (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"
    - "a deep convolutional neural network for background subtraction"
    - "robust online matrix factorization for dynamic background subtraction"
    - "fast optical flow using dense inverse search"

Connected through hyperedge HE-5 (induced by source paper "glimps a greedy mixed integer approach for super robust matched subspace detection"):
  with members:
    - "deterministic hypothesis generation for robust fitting of multiple structures"
    - "deterministic consensus maximization with biconvex programming"
    - "dual principal component pursuit"
    - "coherent motion segmentation in moving camera videos using optical flow orientations"
    - "high rank matrix completion and subspace clustering with missing data"
    - "online identification and tracking of subspaces from highly incomplete information"
    - "high dimensional matched subspace detection when data are missing"
    - "the power of convex relaxation near optimal matrix completion"

Connected through hyperedge HE-6 (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"
    - "image segmentation in video sequences a probabilistic approach"
    - "support predicted modified cs for recursive robust principal components pursuit"

Connected through hyperedge HE-7 (induced by source paper "global optimality guarantees for nonconvex unsupervised video segmentation"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "background subtraction in real applications challenges current models and future directions"
    - "failing to learn autonomously identifying perception failures for self driving cars"
    - "exact guarantees on the absence of spurious local minima for non negative rank 1 robust principal component analysis"
    - "how much restricted isometry is needed in nonconvex matrix recovery"
    - "matrix completion has no spurious local minimum"
    - "fully convolutional networks for semantic segmentation"
    - "robust principal component analysis"

Connected through hyperedge HE-8 (induced by source paper "compressed dynamic mode decomposition for background modeling"):
  with members:
    - "randomized low rank dynamic mode decomposition for motion detection"
    - "sketching as a tool for numerical linear algebra"
    - "using dynamic mode decomposition for real time background foreground separation in video"
    - "simple and deterministic matrix sketching"
    - "sketched svd recovering spectral features from compressive measurements"
    - "randomized algorithms for matrices and data"
    - "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)