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 "total variation regularized tensor rpca for background subtraction from compressive measurements"):
  Abstract: Background subtraction has been a fundamental and widely studied task in video analysis, with a wide range of applications in video surveillance, teleconferencing, and 3D modeling. Recently, motivated by compressive imaging, background subtraction from compressive measurements (BSCM) is becoming an active research task in video surveillance. In this paper, we propose a novel tensor-based robust principal component analysis (TenRPCA) approach for BSCM by decomposing video frames into backgrounds with spatial-temporal correlations and foregrounds with spatio-temporal continuity in a tensor framework. In this approach, we use 3D total variation to enhance the spatio-temporal continuity of foregrounds, and Tucker decomposition to model the spatio-temporal correlations of video background. Based on this idea, we design a basic tensor RPCA model over the video frames, dubbed as the holistic TenRPCA model. To characterize the correlations among the groups of similar 3D patches of video background, we further design a patch-group-based tensor RPCA model by joint tensor Tucker decompositions of 3D patch groups for modeling the video background. Efficient algorithms using the alternating direction method of multipliers are developed to solve the proposed models. Extensive experiments on simulated and real-world videos demonstrate the superiority of the proposed approaches over the existing state-of-the-art approaches.

Includes members:
  Member "compressive sensing of sparse tensors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rip based performance guarantee for low tubal rank tensor recovery")
  Member "compressed sensing with prior information optimal strategies geometry and bounds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi modal dictionary learning for image separation with application in art investigation")
  Member "an online algorithm for separating sparse and low dimensional signal sequences from their sum":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "provable dynamic robust pca or robust subspace tracking")
  Member "local convergence of an algorithm for subspace identification from partial data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "provable low rank phase retrieval and compressive pca")
  Member "fast and efficient compressive sensing using structurally random matrices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sparse signal recovery via generalized entropy functions minimization")
  Member "surveillance video processing using compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the role and the importance of features for background modeling and foreground detection")
  Member "moving object detection by detecting contiguous outliers in the low rank representation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on learning vehicle detection in satellite video")
  Member "online robust subspace tracking from partial information":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep autoencoders with value at risk thresholding for unsupervised anomaly detection")

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)