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: "sar image despeckling through convolutional neural networks"
  Abstract: In this paper we investigate the use of discriminative model learning through Convolutional Neural Networks (CNNs) for SAR image despeckling. The network uses a residual learning strategy, hence it does not recover the filtered image, but the speckle component, which is then subtracted from the noisy one. Training is carried out by considering a large multitemporal SAR image and its multilook version, in order to approximate a clean image. Experimental results, both on synthetic and real SAR data, show the method to achieve better performance with respect to state-of-the-art techniques.

Connected through hyperedge HE-1 (induced by source paper "a new ratio image based cnn algorithm for sar despeckling"):
  with members:
    - "target adaptive cnn based pansharpening"
    - "sar image despeckling using a convolutional neural network"
    - "unassisted quantitative evaluation of despeckling filters"

Connected through hyperedge HE-2 (induced by source paper "generating high quality visible images from sar images using cnns"):
  with members:
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "sar image despeckling using a convolutional neural network"
    - "image to image translation with conditional adversarial networks"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "colorful image colorization"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "learning a dilated residual network for sar image despeckling"):
  with members:
    - "embedding structured contour and location prior in siamesed fully convolutional networks for road detection"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "enhanced deep residual networks for single image super resolution"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "a neural approach to blind motion deblurring"
    - "accurate image super resolution using very deep convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "sar image despeckling using a convolutional neural network"):
  with members:
    - "clearing the skies a deep network architecture for single image rain removal"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "unassisted quantitative evaluation of despeckling filters"
    - "image to image translation with conditional adversarial networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "fully convolutional networks for semantic segmentation"

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)