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 "background subtraction in real applications challenges current models and future directions"):
  Abstract: Computer vision applications based on videos often require the detection of moving objects in their first step. Background subtraction is then applied in order to separate the background and the foreground. In literature, background subtraction is surely among the most investigated field in computer vision providing a big amount of publications. Most of them concern the application of mathematical and machine learning models to be more robust to the challenges met in videos. However, the ultimate goal is that the background subtraction methods developed in research could be employed in real applications like traffic surveillance. But looking at the literature, we can remark that there is often a gap between the current methods used in real applications and the current methods in fundamental research. In addition, the videos evaluated in large-scale datasets are not exhaustive in the way that they only covered a part of the complete spectrum of the challenges met in real applications. In this context, we attempt to provide the most exhaustive survey as possible on real applications that used background subtraction in order to identify the real challenges met in practice, the current used background models and to provide future directions. Thus, challenges are investigated in terms of camera, foreground objects and environments. In addition, we identify the background models that are effectively used in these applications in order to find potential usable recent background models in terms of robustness, time and memory requirements.

Includes members:
  Member "on deterministic sampling patterns for robust low rank matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "weighted matrix completion from non random non uniform sampling patterns")
  Member "a comparison between background modelling methods for vehicle segmentation in highway traffic videos":
    also connected through other hyperedges:
      (none)
  Member "recognition in terra incognita":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rethinking zero shot video classification end to end training for realistic applications")
  Member "robust subspace learning robust pca robust subspace tracking and robust subspace recovery":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "moving objects detection with a moving camera a comprehensive review")
  Member "on the role and the importance of features for background modeling and foreground detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "moving objects detection with a moving camera a comprehensive review")
  Member "towards benchmarking scene background initialization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "moving objects detection with a moving camera a comprehensive review")
  Member "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "moving objects detection with a moving camera a comprehensive review")
  Member "image segmentation in video sequences a probabilistic approach":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "moving objects detection with a moving camera a comprehensive review")

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)