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: "deep de aliasing for fast compressive sensing mri"
  Abstract: Fast Magnetic Resonance Imaging (MRI) is highly in demand for many clinical applications in order to reduce the scanning cost and improve the patient experience. This can also potentially increase the image quality by reducing the motion artefacts and contrast washout. However, once an image field of view and the desired resolution are chosen, the minimum scanning time is normally determined by the requirement of acquiring sufficient raw data to meet the Nyquist-Shannon sampling criteria. Compressive Sensing (CS) theory has been perfectly matched to the MRI scanning sequence design with much less required raw data for the image reconstruction. Inspired by recent advances in deep learning for solving various inverse problems, we propose a conditional Generative Adversarial Networks-based deep learning framework for de-aliasing and reconstructing MRI images from highly undersampled data with great promise to accelerate the data acquisition process. By coupling an innovative content loss with the adversarial loss our de-aliasing results are more realistic. Furthermore, we propose a refinement learning procedure for training the generator network, which can stabilise the training with fast convergence and less parameter tuning. We demonstrate that the proposed framework outperforms state-of-the-art CS-MRI methods, in terms of reconstruction error and perceptual image quality. In addition, our method can reconstruct each image in 0.22ms--0.37ms, which is promising for real-time applications.

Connected through hyperedge HE-1 (induced by source paper "motion artifact reduction using a convolutional neural network for dynamic contrast enhanced mr imaging of the liver"):
  with members:
    - "image reconstruction by domain transform manifold learning"
    - "deep artifact learning for compressed sensing and parallel mri"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "super learning a supervised unsupervised framework for low dose ct image reconstruction"):
  with members:
    - "image reconstruction by domain transform manifold learning"
    - "stochastic deep compressive sensing for the reconstruction of diffusion tensor cardiac mri"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "deep convolutional neural network for inverse problems in imaging"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"):
  with members:
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "improved training of wasserstein gans"
    - "medical image synthesis with context aware generative adversarial networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "neural photo editing with introspective adversarial networks"
    - "nips 2016 tutorial generative adversarial networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"):
  with members:
    - "deep generative adversarial networks for compressed sensing automates mri"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "deep artifact learning for compressed sensing and parallel mri"
    - "deep convolutional neural network for inverse problems in imaging"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "deep residual learning for image recognition"
    - "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)