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 using a convolutional neural network"
  Abstract: Synthetic Aperture Radar (SAR) images are often contaminated by a multiplicative noise known as speckle. Speckle makes the processing and interpretation of SAR images difficult. We propose a deep learning-based approach called, Image Despeckling Convolutional Neural Network (ID-CNN), for automatically removing speckle from the input noisy images. In particular, ID-CNN uses a set of convolutional layers along with batch normalization and rectified linear unit (ReLU) activation function and a component-wise division residual layer to estimate speckle and it is trained in an end-to-end fashion using a combination of Euclidean loss and Total Variation (TV) loss. Extensive experiments on synthetic and real SAR images show that the proposed method achieves significant improvements over the state-of-the-art speckle reduction methods.

Connected through hyperedge HE-1 (induced by source paper "efficiently utilizing complex valued polsar image data via a multi task deep learning framework"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "polarimetric convolutional network for polsar image classification"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "hierarchical representations for efficient architecture search"
    - "mask r cnn"

Connected through hyperedge HE-2 (induced by source paper "optical fringe patterns filtering based on multi stage convolution neural network"):
  with members:
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "u net convolutional networks for biomedical image segmentation"
    - "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"
    - "compression artifacts reduction by a deep convolutional network"
    - "empirical evaluation of rectified activations in convolutional network"

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

Connected through hyperedge HE-4 (induced by source paper "generating high quality visible images from sar images using cnns"):
  with members:
    - "sar image despeckling through convolutional neural networks"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "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"

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)