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: "target adaptive cnn based pansharpening"
  Abstract: We recently proposed a convolutional neural network (CNN) for remote sensing image pansharpening obtaining a significant performance gain over the state of the art. In this paper, we explore a number of architectural and training variations to this baseline, achieving further performance gains with a lightweight network that trains very fast. Leveraging on this latter property, we propose a target-adaptive usage modality that ensures a very good performance also in the presence of a mismatch with respect to the training set and even across different sensors. The proposed method, published online as an off-the-shelf software tool, allows users to perform fast and high-quality CNN-based pansharpening of their own target images on general-purpose hardware.

Connected through hyperedge HE-1 (induced by source paper "s3 a spectral spatial structure loss for pan sharpening networks"):
  with members:
    - "super resolution of sentinel 2 images learning a globally applicable deep neural network"
    - "decoupled weight decay regularization"
    - "enhanced deep residual networks for single image super resolution"
    - "densely connected convolutional networks"
    - "accurate image super resolution using very deep convolutional networks"
    - "deep residual learning for image recognition"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-2 (induced by source paper "advances on cnn based super resolution of sentinel 2 images"):
  with members:
    - "super resolution of sentinel 2 images learning a globally applicable deep neural network"
    - "accurate image super resolution using very deep convolutional networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "image super resolution using deep convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "a new ratio image based cnn algorithm for sar despeckling"):
  with members:
    - "sar image despeckling through convolutional neural networks"
    - "sar image despeckling using a convolutional neural network"
    - "unassisted quantitative evaluation of despeckling filters"

Connected through hyperedge HE-4 (induced by source paper "a cnn based super resolution technique for active fire detection on sentinel 2 data"):
  with members:
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "an overview of gradient descent optimization algorithms"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "psgan a generative adversarial network for remote sensing image pan sharpening"):
  with members:
    - "learning continuous face age progression a pyramid of gans"
    - "boosting the accuracy of multispectral image pansharpening by learning a deep residual network"
    - "remote sensing image fusion based on two stream fusion network"
    - "multi resolution data fusion for super resolution electron microscopy"
    - "accelerating the super resolution convolutional neural network"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-6 (induced by source paper "hybrid noise removal in hyperspectral imagery with a spatial spectral gradient network"):
  with members:
    - "hyperspectral image denoising employing a spatial spectral deep residual convolutional neural network"
    - "missing data reconstruction in remote sensing image with a unified spatial temporal spectral deep convolutional neural network"
    - "boosting the accuracy of multispectral image pansharpening by learning a deep residual network"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "learning a dilated residual network for sar image despeckling"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "denoising hyperspectral image with non i i d noise structure"
    - "image restoration using convolutional auto encoders with symmetric skip connections"

Connected through hyperedge HE-7 (induced by source paper "super resolution of sentinel 2 images learning a globally applicable deep neural network"):
  with members:
    - "enhanced deep residual networks for single image super resolution"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "densely connected convolutional networks"
    - "accurate image super resolution using very deep convolutional networks"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

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)