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: "medical image synthesis with context aware generative adversarial networks"
  Abstract: Computed tomography (CT) is critical for various clinical applications, e.g., radiotherapy treatment planning and also PET attenuation correction. However, CT exposes radiation during acquisition, which may cause side effects to patients. Compared to CT, magnetic resonance imaging (MRI) is much safer and does not involve any radiations. Therefore, recently, researchers are greatly motivated to estimate CT image from its corresponding MR image of the same subject for the case of radiotherapy planning. In this paper, we propose a data-driven approach to address this challenging problem. Specifically, we train a fully convolutional network to generate CT given an MR image. To better model the nonlinear relationship from MRI to CT and to produce more realistic images, we propose to use the adversarial training strategy and an image gradient difference loss function. We further apply AutoContext Model to implement a context-aware generative adversarial network. Experimental results show that our method is accurate and robust for predicting CT images from MRI images, and also outperforms three state-of-the-art methods under comparison.

Connected through hyperedge HE-1 (induced by source paper "boundary weighted domain adaptive neural network for prostate mr image segmentation"):
  with members:
    - "fully convolutional adaptation networks for semantic segmentation"
    - "learning to adapt structured output space for semantic segmentation"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "a survey on deep transfer learning"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "deep contextualized word representations"
    - "video salient object detection via fully convolutional networks"
    - "adversarial learning for semi supervised semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "infinite brain mr images pggan based data augmentation for tumor detection"):
  with members:
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "brain tumor segmentation with deep neural networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "how to fool radiologists with generative adversarial networks a visual turing test for lung cancer diagnosis"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "red blood cell image generation for data augmentation using conditional generative adversarial networks"):
  with members:
    - "medgan medical image translation using gans"
    - "learning more with less conditional pggan based data augmentation for brain metastases detection using highly rough annotation on mr images"
    - "generative adversarial network in medical imaging a review"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "deep adversarial context aware landmark detection for ultrasound imaging"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "high resolution medical image synthesis using progressively grown generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "inspirational adversarial image generation"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "a style based generator architecture for generative adversarial networks"
    - "deja vu an empirical evaluation of the memorization properties of convnets"
    - "which training methods for gans do actually converge"
    - "progressive growing of gans for improved quality stability and variation"
    - "arbitrary style transfer in real time with adaptive instance normalization"

Connected through hyperedge HE-5 (induced by source paper "approximating human judgment of generated image quality"):
  with members:
    - "unifying human and statistical evaluation for natural language generation"
    - "hype a benchmark for human eye perceptual evaluation of generative models"
    - "gansynth adversarial neural audio synthesis"
    - "pros and cons of gan evaluation measures"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "generating adversarial examples with adversarial networks"
    - "pieapp perceptual image error assessment through pairwise preference"

Connected through hyperedge HE-6 (induced by source paper "establishing an evaluation metric to quantify climate change image realism"):
  with members:
    - "on the evaluation of conditional gans"
    - "hype a benchmark for human eye perceptual evaluation of generative models"
    - "do imagenet classifiers generalize to imagenet"
    - "do gans leave artificial fingerprints"
    - "multimodal unsupervised image to image translation"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "ward2icu a vital signs dataset of inpatients from the general ward"):
  with members:
    - "torchgan a flexible framework for gan training and evaluation"
    - "biosignal generation and latent variable analysis with recurrent generative adversarial networks"
    - "generation of synthetic electronic medical record text"
    - "generating multi label discrete patient records using generative adversarial networks"
    - "improved training of wasserstein gans"
    - "least squares generative adversarial networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "empirical evaluation of rectified activations in convolutional network"

Connected through hyperedge HE-8 (induced by source paper "unsupervised learning for cell level visual representation in histopathology images with generative adversarial networks"):
  with members:
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "improved training of wasserstein gans"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "dcan deep contour aware networks for accurate gland segmentation"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep residual learning for image recognition"
    - "unsupervised deep embedding for clustering analysis"

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)