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: "denoising of 3d magnetic resonance images with multi channel residual learning of convolutional neural network"
  Abstract: The denoising of magnetic resonance (MR) images is a task of great importance for improving the acquired image quality. Many methods have been proposed in the literature to retrieve noise free images with good performances. Howerever, the state-of-the-art denoising methods, all needs a time-consuming optimization processes and their performance strongly depend on the estimated noise level parameter. Within this manuscript we propose the idea of denoising MRI Rician noise using a convolutional neural network. The advantage of the proposed methodology is that the learning based model can be directly used in the denosing process without optimization and even without the noise level parameter. Specifically, a ten convolutional layers neural network combined with residual learning and multi-channel strategy was proposed. Two training ways: training on a specific noise level and training on a general level were conducted to demonstrate the capability of our methods. Experimental results over synthetic and real 3D MR data demonstrate our proposed network can achieve superior performance compared with other methods in term of both of the peak signal to noise ratio and the global of structure similarity index. Without noise level parameter, our general noise-applicable model is also better than the other compared methods in two datasets. Furthermore, our training model shows good general applicability.

Connected through hyperedge HE-1 (induced by source paper "compressed sensing mri via a multi scale dilated residual convolution network"):
  with members:
    - "deep learning cardiac motion analysis for human survival prediction"
    - "image super resolution using very deep residual channel attention networks"
    - "a multiscale image denoising algorithm based on dilated residual convolution network"
    - "deep residual learning for accelerated mri using magnitude and phase networks"
    - "depth reconstruction of translucent objects from a single time of flight camera using deep residual networks"
    - "lightweight pyramid networks for image deraining"
    - "a segmentation aware deep fusion network for compressed sensing mri"
    - "isointense infant brain mri segmentation with a dilated convolutional neural network"

Connected through hyperedge HE-2 (induced by source paper "automated segmentation of lesions in ultrasound using semi pixel wise cycle generative adversarial nets"):
  with members:
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "hough cnn deep learning for segmentation of deep brain regions in mri and ultrasound"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "image synthesis in multi contrast mri with conditional generative adversarial networks"):
  with members:
    - "an introduction to image synthesis with generative adversarial nets"
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"
    - "speeding up the hyperparameter optimization of deep convolutional neural networks"
    - "synthesis of positron emission tomography pet images via multi channel generative adversarial networks gans"
    - "freehand ultrasound image simulation with spatially conditioned generative adversarial networks"
    - "virtual pet images from ct data using deep convolutional networks initial results"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "a deep learning approach to denoise optical coherence tomography images of the optic nerve head"):
  with members:
    - "classification of mri data using deep learning and gaussian process based model selection"
    - "retinal fluid segmentation and detection in optical coherence tomography images using fully convolutional neural network"
    - "relaynet retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional network"
    - "richer convolutional features for edge detection"
    - "imagej2 imagej for the next generation of scientific image data"
    - "medical image denoising using convolutional denoising autoencoders"
    - "multiframe denoising of high speed optical coherence tomography data using interframe and intraframe priors"
    - "u net convolutional networks for biomedical image 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)