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 "moving objects detection with a moving camera a comprehensive review"):
  Abstract: During about 30 years, a lot of research teams have worked on the big challenge of detection of moving objects in various challenging environments. First applications concern static cameras but with the rise of the mobile sensors studies on moving cameras have emerged over time. In this survey, we propose to identify and categorize the different existing methods found in the literature. For this purpose, we propose to classify these methods according to the choose of the scene representation: one plane or several parts. Inside these two categories, the methods are grouped according to eight different approaches: panoramic background subtraction, dual cameras, motion compensation, subspace segmentation, motion segmentation, plane+parallax, multi planes and split image in blocks. A reminder of methods for static cameras is provided as well as the challenges with both static and moving cameras. Publicly available datasets and evaluation metrics are also surveyed in this paper.

Includes members:
  Member "deeppbm deep probabilistic background model estimation from video sequences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "g lbm generative low dimensional background model estimation from video sequences")
  Member "the 2019 davis challenge on vos unsupervised multi object segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "combining background subtraction algorithms with convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bsuv net a fully convolutional neural network for background subtraction of unseen videos")
  Member "background subtraction in real applications challenges current models and future directions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "graphbgs background subtraction via recovery of graph signals")
  Member "learning multi scale features for foreground segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep autoencoders with value at risk thresholding for unsupervised anomaly detection")
  Member "deep neural network concepts for background subtraction a systematic review and comparative evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "graphbgs background subtraction via recovery of graph signals")
  Member "foreground segmentation using convolutional neural networks for multiscale feature encoding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "graphbgs background subtraction via recovery of graph signals")
  Member "are object detection assessment criteria ready for maritime computer vision":
    also connected through other hyperedges:
      (none)

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)