human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "graphbgs background subtraction via recovery of graph signals"):
  Abstract: Graph-based algorithms have been successful approaching the problems of unsupervised and semi-supervised learning. Recently, the theory of graph signal processing and semi-supervised learning have been combined leading to new developments and insights in the field of machine learning. In this paper, concepts of recovery of graph signals and semi-supervised learning are introduced in the problem of background subtraction. We propose a new algorithm named GraphBGS, this method uses a Mask R-CNN for instances segmentation; temporal median filter for background initialization; motion, texture, color, and structural features for representing the nodes of a graph; k-nearest neighbors for the construction of the graph; and finally a semi-supervised method inspired from the theory of recovery of graph signals to solve the problem of background subtraction. The method is evaluated on the publicly available change detection, and scene background initialization databases. Experimental results show that GraphBGS outperforms unsupervised background subtraction algorithms in some challenges of the change detection dataset. And most significantly, this method outperforms generative adversarial networks in unseen videos in some sequences of the scene background initialization database.

Includes members:
  Member "background subtraction in real applications challenges current models and future directions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "moving objects detection with a moving camera a comprehensive review")
  Member "a fully convolutional neural network for background subtraction of unseen videos":
    also connected through other hyperedges:
      (none)
  Member "deep neural network concepts for background subtraction a systematic review and comparative evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "moving objects detection with a moving camera a comprehensive review")
  Member "foreground segmentation using convolutional neural networks for multiscale feature encoding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "moving objects detection with a moving camera a comprehensive review")
  Member "a robust background initialization algorithm with superpixel motion detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "g lbm generative low dimensional background model estimation from video sequences")
  Member "background subtraction with real time semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "moving objects detection with a moving camera a comprehensive review")
  Member "mask r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "efficient sampling set selection for bandlimited graph signals using graph spectral proxies":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "edgenets edge varying graph neural networks")

Question: Please tell me which class the source paper 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)