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: "tree structure bayesian compressive sensing for video"
  Abstract: A Bayesian compressive sensing framework is developed for video reconstruction based on the color coded aperture compressive temporal imaging (CACTI) system. By exploiting the three dimension (3D) tree structure of the wavelet and Discrete Cosine Transformation (DCT) coefficients, a Bayesian compressive sensing inversion algorithm is derived to reconstruct (up to 22) color video frames from a single monochromatic compressive measurement. Both simulated and real datasets are adopted to verify the performance of the proposed algorithm.

Connected through hyperedge HE-1 (induced by source paper "image compression based on compressive sensing end to end comparison with jpeg"):
  with members:
    - "nonlocal low rank tensor factor analysis for image restoration"
    - "block wise lensless compressive camera"
    - "fundamental distortion limits of analog to digital compression"
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "generalized alternating projection based total variation minimization for compressive sensing"
    - "compressive hyperspectral imaging with side information"
    - "from denoising to compressed sensing"
    - "compressive measurements generated by structurally random matrices asymptotic normality and quantization"

Connected through hyperedge HE-2 (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"
    - "lensless imaging by compressive sensing"
    - "coded aperture compressive temporal imaging"
    - "multi view in lensless compressive imaging"
    - "surveillance video processing using compressive sensing"

Connected through hyperedge HE-3 (induced by source paper "convergence of the generalized alternating projection algorithm for compressive sensing"):
  with members:
    - "lensless compressive imaging"
    - "compressive hyperspectral imaging with side information"
    - "lensless imaging by compressive sensing"
    - "multi view in lensless compressive 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)