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 object detection in video using saliency map and subspace learning"):
  Abstract: Moving object detection is a key to intelligent video analysis. On the one hand, what moves is not only interesting objects but also noise and cluttered background. On the other hand, moving objects without rich texture are prone not to be detected. So there are undesirable false alarms and missed alarms in many algorithms of moving object detection. To reduce the false alarms and missed alarms, in this paper, we propose to incorporate a saliency map into an incremental subspace analysis framework where the saliency map makes estimated background has less chance than foreground (i.e., moving objects) to contain salient objects. The proposed objective function systematically takes account into the properties of sparsity, low-rank, connectivity, and saliency. An alternative minimization algorithm is proposed to seek the optimal solutions. Experimental results on the Perception Test Images Sequences demonstrate that the proposed method is effective in reducing false alarms and missed alarms.

Includes members:
  Member "inner and inter label propagation salient object detection in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "edge preserving and multi scale contextual neural network for salient object detection")
  Member "background subtraction via generalized fused lasso foreground modeling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "g lbm generative low dimensional background model estimation from video sequences")
  Member "corola a sequential solution to moving object detection using low rank approximation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online structured sparsity based moving object detection from satellite videos")
  Member "low rank modeling and its applications in image analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a comprehensive survey for low rank regularization")
  Member "generalized principal component analysis gpca":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "salient object detection a survey")
  Member "moving object detection by detecting contiguous outliers in the low rank representation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on learning vehicle detection in satellite video")
  Member "robust principal component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")

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)