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 "blind image fusion for hyperspectral imaging with the directional total variation"):
  Abstract: Hyperspectral imaging is a cutting-edge type of remote sensing used for mapping vegetation properties, rock minerals and other materials. A major drawback of hyperspectral imaging devices is their intrinsic low spatial resolution. In this paper, we propose a method for increasing the spatial resolution of a hyperspectral image by fusing it with an image of higher spatial resolution that was obtained with a different imaging modality. This is accomplished by solving a variational problem in which the regularization functional is the directional total variation. To accommodate for possible mis-registrations between the two images, we consider a non-convex blind super-resolution problem where both a fused image and the corresponding convolution kernel are estimated. Using this approach, our model can realign the given images if needed. Our experimental results indicate that the non-convexity is negligible in practice and that reliable solutions can be computed using a variety of different optimization algorithms. Numerical results on real remote sensing data from plant sciences and urban monitoring show the potential of the proposed method and suggests that it is robust with respect to the regularization parameters, mis-registration and the shape of the kernel.

Includes members:
  Member "a haar wavelet based perceptual similarity index for image quality assessment":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the oracle of dlphi")
  Member "a survey of pansharpening methods with a new band decoupled variational model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "restoration of pansharpened images by conditional filtering in the pca domain")
  Member "nonparametric image registration of airborne lidar hyperspectral and photographic imagery of wooded landscapes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multisource region attention network for fine grained object recognition in remote sensing imagery")
  Member "hyperspectral pansharpening a review":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised and unregistered hyperspectral image super resolution with mutual dirichlet net")
  Member "a convex formulation for hyperspectral image superresolution via subspace based regularization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised and unregistered hyperspectral image super resolution with mutual dirichlet net")
  Member "enhanced compressed sensing recovery with level set normals":
    also connected through other hyperedges:
      (none)

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)