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: "towards adversarial retinal image synthesis"
  Abstract: Synthesizing images of the eye fundus is a challenging task that has been previously approached by formulating complex models of the anatomy of the eye. New images can then be generated by sampling a suitable parameter space. In this work, we propose a method that learns to synthesize eye fundus images directly from data. For that, we pair true eye fundus images with their respective vessel trees, by means of a vessel segmentation technique. These pairs are then used to learn a mapping from a binary vessel tree to a new retinal image. For this purpose, we use a recent image-to-image translation technique, based on the idea of adversarial learning. Experimental results show that the original and the generated images are visually different in terms of their global appearance, in spite of sharing the same vessel tree. Additionally, a quantitative quality analysis of the synthetic retinal images confirms that the produced images retain a high proportion of the true image set quality.

Connected through hyperedge HE-1 (induced by source paper "ophthalmic diagnosis using deep learning with fundus images a critical review"):
  with members:
    - "pixelbnn augmenting the pixelcnn with batch normalization and the presentation of a fast architecture for retinal vessel segmentation"
    - "embedded deep learning in ophthalmology making ophthalmic imaging smarter"
    - "classification of findings with localized lesions in fundoscopic images using a regionally guided cnn"
    - "deep supervision with additional labels for retinal vessel segmentation task"
    - "deep visual domain adaptation a survey"
    - "predicting optical coherence tomography derived diabetic macular edema grades from fundus photographs using deep learning"
    - "deep learning based retinal oct segmentation"
    - "disc aware ensemble network for glaucoma screening from fundus image"

Connected through hyperedge HE-2 (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"
    - "generative adversarial training for mra image synthesis using multi contrast mri"
    - "image synthesis in multi contrast mri with conditional generative adversarial networks"
    - "simulating patho realistic ultrasound images using deep generative networks with adversarial learning"

Connected through hyperedge HE-3 (induced by source paper "synthetic image augmentation for improved classification using generative adversarial networks"):
  with members:
    - "identifying object states in cooking related images"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "tensorflow a system for large scale machine learning"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "semi supervised adversarial learning to generate photorealistic face images of new identities from 3d morphable model"):
  with members:
    - "watergan unsupervised generative network to enable real time color correction of monocular underwater images"
    - "began boundary equilibrium generative adversarial networks"
    - "sphereface deep hypersphere embedding for face recognition"
    - "progressive growing of gans for improved quality stability and variation"
    - "learning deep convolutional embeddings for face representation using joint sample and set based supervision"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "bighand2 2m benchmark hand pose dataset and state of the art analysis"
    - "a generative model of people in clothing"

Connected through hyperedge HE-5 (induced by source paper "photographic text to image synthesis with a hierarchically nested adversarial network"):
  with members:
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "tell me where to look guided attention inference network"
    - "context encoding for semantic segmentation"
    - "dual discriminator generative adversarial nets"
    - "began boundary equilibrium generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "semantic image synthesis via adversarial learning"
    - "multi style generative network for real time transfer"

Connected through hyperedge HE-6 (induced by source paper "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"):
  with members:
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "densely connected pyramid dehazing network"
    - "began boundary equilibrium generative adversarial networks"
    - "tandemnet distilling knowledge from medical images using diagnostic reports as optional semantic references"
    - "learning compositional visual concepts with mutual consistency"
    - "gans for biological image synthesis"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"

Connected through hyperedge HE-7 (induced by source paper "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"):
  with members:
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "virtual pet images from ct data using deep convolutional networks initial results"
    - "a survey on deep learning in medical image analysis"
    - "scan structure correcting adversarial network for organ segmentation in chest x rays"
    - "medical image synthesis with context aware generative adversarial networks"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"

Connected through hyperedge HE-8 (induced by source paper "synseg net synthetic segmentation without target modality ground truth"):
  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"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "gans for biological image synthesis"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a survey on deep learning in medical image analysis"
    - "splenomegaly segmentation using global convolutional kernels and conditional generative adversarial networks"
    - "adversarial networks for the detection of aggressive prostate cancer"

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)