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 mr to ct synthesis using unpaired data"
  Abstract: MR-only radiotherapy treatment planning requires accurate MR-to-CT synthesis. Current deep learning methods for MR-to-CT synthesis depend on pairwise aligned MR and CT training images of the same patient. However, misalignment between paired images could lead to errors in synthesized CT images. To overcome this, we propose to train a generative adversarial network (GAN) with unpaired MR and CT images. A GAN consisting of two synthesis convolutional neural networks (CNNs) and two discriminator CNNs was trained with cycle consistency to transform 2D brain MR image slices into 2D brain CT image slices and vice versa. Brain MR and CT images of 24 patients were analyzed. A quantitative evaluation showed that the model was able to synthesize CT images that closely approximate reference CT images, and was able to outperform a GAN model trained with paired MR and CT images.

Connected through hyperedge HE-1 (induced by source paper "medgan medical image translation using gans"):
  with members:
    - "generative adversarial network in medical imaging a review"
    - "towards a deeper understanding of adversarial losses"
    - "improving surgical training phantoms by hyperrealism deep unpaired image to image translation from real surgeries"
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "adversarial sparse view cbct artifact reduction"
    - "voxelatlasgan 3d left ventricle segmentation on echocardiography with atlas guided generation and voxel to voxel discrimination"
    - "cross modality synthesis from ct to pet using fcn and gan networks for improved automated lesion detection"
    - "pros and cons of gan evaluation measures"

Connected through hyperedge HE-2 (induced by source paper "kernel of cyclegan as a principle homogeneous space"):
  with members:
    - "miso mutual information loss with stochastic style representations for multimodal image to image translation"
    - "unsupervised image to image translation networks"
    - "mr based synthetic ct generation using a deep convolutional neural network method"
    - "density estimation using real nvp"
    - "a neural algorithm of artistic style"

Connected through hyperedge HE-3 (induced by source paper "cross modality ct mri prior augmented deep learning for robust lung tumor segmentation from small mr datasets"):
  with members:
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "distribution matching losses can hallucinate features in medical image translation"
    - "building medical image classifiers with very limited data using segmentation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "adversarial synthesis learning enables segmentation without target modality ground truth"
    - "learning from simulated and unsupervised images through adversarial training"

Connected through hyperedge HE-4 (induced by source paper "visual enhancement of cone beam ct by use of cyclegan"):
  with members:
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "deep learning in radiology an overview of the concepts and a survey of the state of the art"
    - "on the effectiveness of least squares generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "instance normalization the missing ingredient for fast stylization"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "refacing reconstructing anonymized facial features using gans"):
  with members:
    - "generative adversarial network in medical imaging a review"
    - "generative adversarial networks for image to image translation on multi contrast mr images a comparison of cyclegan and unit"
    - "gans for medical image analysis"
    - "learning myelin content in multiple sclerosis from multimodal mri through adversarial training"
    - "image synthesis in multi contrast mri with conditional generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a survey on deep learning in medical image analysis"
    - "medical image synthesis with context aware generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "generative image translation for data augmentation of bone lesion pathology"):
  with members:
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "bagan data augmentation with balancing gan"
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"
    - "dilated residual networks"
    - "unsupervised image to image translation networks"
    - "generalization of deep neural networks for chest pathology classification in x rays using generative adversarial networks"
    - "unsupervised domain adaptation in brain lesion segmentation with adversarial networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "mri to ct translation with gans"):
  with members:
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "image to image translation with conditional adversarial networks"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "ct synthesis from mr images for orthopedic applications in the lower arm using a conditional generative adversarial network"):
  with members:
    - "mr based synthetic ct generation using a deep convolutional neural network method"
    - "medical image synthesis with context aware generative adversarial networks"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "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)