human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "generating diffusion mri scalar maps from t1 weighted images using generative adversarial networks"):
  Abstract: Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive microstructure assessment technique. Scalar measures, such as FA (fractional anisotropy) and MD (mean diffusivity), quantifying micro-structural tissue properties can be obtained using diffusion models and data processing pipelines. However, it is costly and time consuming to collect high quality diffusion data. Here, we therefore demonstrate how Generative Adversarial Networks (GANs) can be used to generate synthetic diffusion scalar measures from structural T1-weighted images in a single optimized step. Specifically, we train the popular CycleGAN model to learn to map a T1 image to FA or MD, and vice versa. As an application, we show that synthetic FA images can be used as a target for non-linear registration, to correct for geometric distortions common in diffusion MRI.

Includes members:
  Member "synergistic reconstruction and synthesis via generative adversarial networks for accelerated multi contrast mri":
    also connected through other hyperedges:
      (none)
  Member "generative adversarial networks for image to image translation on multi contrast mr images a comparison of cyclegan and unit":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semi supervised learning approach to generate neuroimaging modalities with adversarial training")
  Member "distribution matching losses can hallucinate features in medical image translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "visualisation of medical image fusion and translation for accurate diagnosis of high grade gliomas")
  Member "gans for medical image analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "red blood cell image generation for data augmentation using conditional generative adversarial networks")
  Member "generative adversarial training for mra image synthesis using multi contrast mri":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hi net hybrid fusion network for multi modal mr image synthesis")
  Member "image synthesis in multi contrast mri with conditional generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "medgan medical image translation using gans")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "deep mr to ct synthesis using unpaired data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "medgan medical image translation using gans")

Question: Please tell me which class the source paper 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)