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 "g lbm generative low dimensional background model estimation from video sequences"):
  Abstract: In this paper, we propose a computationally tractable and theoretically supported non-linear low-dimensional generative model to represent real-world data in the presence of noise and sparse outliers. The non-linear low-dimensional manifold discovery of data is done through describing a joint distribution over observations, and their low-dimensional representations (i.e. manifold coordinates). Our model, called generative low-dimensional background model (G-LBM) admits variational operations on the distribution of the manifold coordinates and simultaneously generates a low-rank structure of the latent manifold given the data. Therefore, our probabilistic model contains the intuition of the non-probabilistic low-dimensional manifold learning. G-LBM selects the intrinsic dimensionality of the underling manifold of the observations, and its probabilistic nature models the noise in the observation data. G-LBM has direct application in the background scenes model estimation from video sequences and we have evaluated its performance on SBMnet-2016 and BMC2012 datasets, where it achieved a performance higher or comparable to other state-of-the-art methods while being agnostic to the background scenes in videos. Besides, in challenges such as camera jitter and background motion, G-LBM is able to robustly estimate the background by effectively modeling the uncertainties in video observations in these scenarios.

Includes members:
  Member "deeppbm deep probabilistic background model estimation from video sequences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "moving objects detection with a moving camera a comprehensive review")
  Member "background subtraction in real applications challenges current models and future directions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "moving objects detection with a moving camera a comprehensive review")
  Member "deep neural network concepts for background subtraction a systematic review and comparative evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (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-5 (induced by source paper "graphbgs background subtraction via recovery of graph signals")
  Member "neural collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "advances in collaborative filtering and ranking")
  Member "background subtraction via fast robust matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "moving objects detection with a moving camera a comprehensive review")
  Member "learning features by watching objects move":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning video object segmentation from unlabeled videos")

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)