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: "bayesian fusion of multi band images"
  Abstract: In this paper, a Bayesian fusion technique for remotely sensed multi-band images is presented. The observed images are related to the high spectral and high spatial resolution image to be recovered through physical degradations, e.g., spatial and spectral blurring and/or subsampling defined by the sensor characteristics. The fusion problem is formulated within a Bayesian estimation framework. An appropriate prior distribution exploiting geometrical consideration is introduced. To compute the Bayesian estimator of the scene of interest from its posterior distribution, a Markov chain Monte Carlo algorithm is designed to generate samples asymptotically distributed according to the target distribution. To efficiently sample from this high-dimension distribution, a Hamiltonian Monte Carlo step is introduced in the Gibbs sampling strategy. The efficiency of the proposed fusion method is evaluated with respect to several state-of-the-art fusion techniques. In particular, low spatial resolution hyperspectral and multispectral images are fused to produce a high spatial resolution hyperspectral image.

Connected through hyperedge HE-1 (induced by source paper "inversenet solving inverse problems with splitting networks"):
  with members:
    - "one network to solve them all solving linear inverse problems using deep projection models"
    - "deep convolutional neural network for inverse problems in imaging"
    - "an inner loop free solution to inverse problems using deep neural networks"
    - "a deep cascade of convolutional neural networks for mr image reconstruction"
    - "photographic image synthesis with cascaded refinement networks"
    - "learning representations for automatic colorization"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "amortised map inference for image super resolution"

Connected through hyperedge HE-2 (induced by source paper "an inner loop free solution to inverse problems using deep neural networks"):
  with members:
    - "one network to solve them all solving linear inverse problems using deep projection models"
    - "a deep cascade of convolutional neural networks for mr image reconstruction"
    - "learning representations for automatic colorization"
    - "amortised map inference for image super resolution"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "r fuse robust fast fusion of multiband images based on solving a sylvester equation"
    - "plug play generative networks conditional iterative generation of images in latent space"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"

Connected through hyperedge HE-3 (induced by source paper "fusing multiple multiband images"):
  with members:
    - "hyperspectral image recovery via hybrid regularization"
    - "multiband image fusion based on spectral unmixing"
    - "r fuse robust fast fusion of multiband images based on solving a sylvester equation"
    - "fast and robust pushbroom hyperspectral imaging via dmd based scanning"
    - "hyperspectral pansharpening a review"
    - "a convex formulation for hyperspectral image superresolution via subspace based regularization"
    - "fast fusion of multi band images based on solving a sylvester equation"
    - "hyperspectral and multispectral image fusion based on a sparse representation"

Connected through hyperedge HE-4 (induced by source paper "exploiting restricted boltzmann machines and deep belief networks in compressed sensing"):
  with members:
    - "approximate message passing with restricted boltzmann machine priors"
    - "sparse approximation based on a random overcomplete basis"
    - "variational bayesian algorithm for quantized compressed sensing"
    - "cramer rao type bounds for sparse bayesian learning"
    - "exploiting statistical dependencies in sparse representations for signal recovery"
    - "structured compressed sensing from theory to applications"
    - "block sparse signals uncertainty relations and efficient recovery"

Connected through hyperedge HE-5 (induced by source paper "remote sensing image fusion based on two stream fusion network"):
  with members:
    - "u net convolutional networks for biomedical image segmentation"
    - "accurate image super resolution using very deep convolutional networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "image super resolution using deep convolutional networks"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "multiband image fusion based on spectral unmixing"):
  with members:
    - "r fuse robust fast fusion of multiband images based on solving a sylvester equation"
    - "hyperspectral pansharpening a review"
    - "a convex formulation for hyperspectral image superresolution via subspace based regularization"
    - "fast spectral unmixing based on dykstra s alternating projection"
    - "fast fusion of multi band images based on solving a sylvester equation"
    - "hyperspectral and multispectral image fusion based on a sparse representation"

Connected through hyperedge HE-7 (induced by source paper "r fuse robust fast fusion of multiband images based on solving a sylvester equation"):
  with members:
    - "hyperspectral pansharpening a review"
    - "a convex formulation for hyperspectral image superresolution via subspace based regularization"
    - "fast fusion of multi band images based on solving a sylvester equation"
    - "hyperspectral and multispectral image fusion based on a sparse representation"
    - "medical image fusion a survey of the state of the art"

Connected through hyperedge HE-8 (induced by source paper "hyperspectral pansharpening a review"):
  with members:
    - "a convex formulation for hyperspectral image superresolution via subspace based regularization"
    - "fast fusion of multi band images based on solving a sylvester equation"
    - "hyperspectral image superresolution an edge preserving convex formulation"
    - "hyperspectral and multispectral image fusion based on a sparse representation"

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)