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 "compressive online robust principal component analysis with optical flow for video foreground background separation"):
  Abstract: In the context of online Robust Principle Component Analysis (RPCA) for the video foreground-background separation, we propose a compressive online RPCA with optical flow that separates recursively a sequence of frames into sparse (foreground) and low-rank (background) components. Our method considers a small set of measurements taken per data vector (frame), which is different from conventional batch RPCA, processing all the data directly. The proposed method also incorporates multiple prior information, namely previous foreground and background frames, to improve the separation and then updates the prior information for the next frame. Moreover, the foreground prior frames are improved by estimating motions between the previous foreground frames using optical flow and compensating the motions to achieve higher quality foreground prior. The proposed method is applied to online video foreground and background separation from compressive measurements. The visual and quantitative results show that our method outperforms the existing methods.

Includes members:
  Member "recursive recovery of sparse signal sequences from compressive measurements a review":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "real time reconstruction of counting process through queues")
  Member "sparse signal reconstruction with multiple side information using adaptive weights for multiview sources":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online decomposition of compressive streaming data using n ell_1 cluster weighted minimization")
  Member "adaptive rate compressive sensing using side information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "side information in robust principal component analysis algorithms and applications")
  Member "an online algorithm for separating sparse and low dimensional signal sequences from their sum":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "provable dynamic robust pca or robust subspace tracking")
  Member "robust pca with partial subspace knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "provable dynamic robust pca or robust subspace tracking")
  Member "recursive robust pca or recursive sparse recovery in large but structured noise":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "provable subspace tracking from missing data and matrix completion")
  Member "compressive principal component pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "modified cs modifying compressive sensing for problems with partially known support":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multilinear compressive learning with prior knowledge")

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)