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: "generative adversarial training for mra image synthesis using multi contrast mri"
  Abstract: Magnetic Resonance Angiography (MRA) has become an essential MR contrast for imaging and evaluation of vascular anatomy and related diseases. MRA acquisitions are typically ordered for vascular interventions, whereas in typical scenarios, MRA sequences can be absent in the patient scans. This motivates the need for a technique that generates inexistent MRA from existing MR multi-contrast, which could be a valuable tool in retrospective subject evaluations and imaging studies. In this paper, we present a generative adversarial network (GAN) based technique to generate MRA from T1-weighted and T2-weighted MRI images, for the first time to our knowledge. To better model the representation of vessels which the MRA inherently highlights, we design a loss term dedicated to a faithful reproduction of vascularities. To that end, we incorporate steerable filter responses of the generated and reference images inside a Huber function loss term. Extending the well- established generator-discriminator architecture based on the recent PatchGAN model with the addition of steerable filter loss, the proposed steerable GAN (sGAN) method is evaluated on the large public database IXI. Experimental results show that the sGAN outperforms the baseline GAN method in terms of an overlap score with similar PSNR values, while it leads to improved visual perceptual quality.

Connected through hyperedge HE-1 (induced by source paper "hi net hybrid fusion network for multi modal mr image synthesis"):
  with members:
    - "skrgan sketching rendering unconditional generative adversarial networks for medical image synthesis"
    - "generative adversarial network in medical imaging a review"
    - "brain tumor segmentation on mri with missing modalities"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "image synthesis in multi contrast mri with conditional generative adversarial networks"
    - "towards adversarial retinal image synthesis"
    - "simulating patho realistic ultrasound images using deep generative networks with adversarial learning"

Connected through hyperedge HE-2 (induced by source paper "generating diffusion mri scalar maps from t1 weighted images using generative adversarial networks"):
  with members:
    - "synergistic reconstruction and synthesis via generative adversarial networks for accelerated multi contrast mri"
    - "generative adversarial networks for image to image translation on multi contrast mr images a comparison of cyclegan and unit"
    - "distribution matching losses can hallucinate features in medical image translation"
    - "gans for medical image analysis"
    - "image synthesis in multi contrast mri with conditional generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "deep mr to ct synthesis using unpaired data"
    - "medical image synthesis with context aware generative adversarial networks"

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)