human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "surveillance video processing using compressive sensing"
  Abstract: A compressive sensing method combined with decomposition of a matrix formed with image frames of a surveillance video into low rank and sparse matrices is proposed to segment the background and extract moving objects in a surveillance video. The video is acquired by compressive measurements, and the measurements are used to reconstruct the video by a low rank and sparse decomposition of matrix. The low rank component represents the background, and the sparse component is used to identify moving objects in the surveillance video. The decomposition is performed by an augmented Lagrangian alternating direction method. Experiments are carried out to demonstrate that moving objects can be reliably extracted with a small amount of measurements.

Connected through hyperedge HE-1 (induced by source paper "on the role and the importance of features for background modeling and foreground detection"):
  with members:
    - "robust 3d action recognition through sampling local appearances and global distributions"
    - "estimating sparse signals with smooth support via convex programming and block sparsity"
    - "deep learning in neural networks"
    - "variational inference for background subtraction in infrared imagery"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "background subtraction separating the modeling and the inference"
    - "adaptive rate compressive sensing using side information"
    - "complex background subtraction by pursuing dynamic spatio temporal models"

Connected through hyperedge HE-2 (induced by source paper "total variation regularized tensor rpca for background subtraction from compressive measurements"):
  with members:
    - "compressive sensing of sparse tensors"
    - "compressed sensing with prior information optimal strategies geometry and bounds"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "local convergence of an algorithm for subspace identification from partial data"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "moving object detection by detecting contiguous outliers in the low rank representation"
    - "online robust subspace tracking from partial information"
    - "online identification and tracking of subspaces from highly incomplete information"

Connected through hyperedge HE-3 (induced by source paper "multi resolution compressive sensing reconstruction"):
  with members:
    - "multi resolution compressed sensing via approximate message passing"
    - "from denoising to compressed sensing"
    - "lensless imaging by compressive sensing"
    - "the stone transform multi resolution image enhancement and real time compressive video"
    - "multi view in lensless compressive imaging"
    - "minimum complexity pursuit for universal compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "can dmd obtain a scene background in color"
    - "efficient background modeling based on sparse representation and outlier iterative removal"
    - "fast algorithms for robust pca via gradient descent"
    - "fast robust pca on graphs"
    - "approximated robust principal component analysis for improved general scene background subtraction"
    - "mpi faun an mpi based framework for alternating updating nonnegative matrix factorization"

Connected through hyperedge HE-5 (induced by source paper "generalized alternating projection based total variation minimization for compressive sensing"):
  with members:
    - "lensless compressive imaging"
    - "compressive hyperspectral imaging with side information"
    - "convergence of the generalized alternating projection algorithm for compressive sensing"
    - "tree structure bayesian compressive sensing for video"
    - "lensless imaging by compressive sensing"
    - "coded aperture compressive temporal imaging"
    - "multi view in lensless compressive imaging"

Connected through hyperedge HE-6 (induced by source paper "lensless compressive imaging"):
  with members:
    - "compressive hyperspectral imaging with side information"
    - "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization"
    - "bayesian nonparametric dictionary learning for compressed sensing mri"
    - "from denoising to compressed sensing"
    - "a new compressive video sensing framework for mobile broadcast"
    - "lensless imaging by compressive sensing"
    - "multi view in lensless compressive imaging"

Connected through hyperedge HE-7 (induced by source paper "a novel tensor robust pca approach for background subtraction from compressive measurements"):
  with members:
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "robust principal component analysis"

Connected through hyperedge HE-8 (induced by source paper "signal to noise ratio in lensless compressive imaging"):
  with members:
    - "a new compressive video sensing framework for mobile broadcast"
    - "lensless imaging by compressive sensing"
    - "multi view in lensless compressive imaging"
    - "lensless compressive sensing imaging"

Question: Please tell me which class the center node 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)