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: "unsupervised histopathology image synthesis"
  Abstract: Hematoxylin and Eosin stained histopathology image analysis is essential for the diagnosis and study of complicated diseases such as cancer. Existing state-of-the-art approaches demand extensive amount of supervised training data from trained pathologists. In this work we synthesize in an unsupervised manner, large histopathology image datasets, suitable for supervised training tasks. We propose a unified pipeline that: a) generates a set of initial synthetic histopathology images with paired information about the nuclei such as segmentation masks; b) refines the initial synthetic images through a Generative Adversarial Network (GAN) to reference styles; c) trains a task-specific CNN and boosts the performance of the task-specific CNN with on-the-fly generated adversarial examples. Our main contribution is that the synthetic images are not only realistic, but also representative (in reference styles) and relatively challenging for training task-specific CNNs. We test our method for nucleus segmentation using images from four cancer types. When no supervised data exists for a cancer type, our method without supervision cost significantly outperforms supervised methods which perform across-cancer generalization. Even when supervised data exists for all cancer types, our approach without supervision cost performs better than supervised methods.

Connected through hyperedge HE-1 (induced by source paper "multi adversarial variational autoencoder networks"):
  with members:
    - "learning implicit generative models with the method of learned moments"
    - "stabilizing gan training with multiple random projections"
    - "spectral normalization for generative adversarial networks"
    - "autoregressive quantile networks for generative modeling"
    - "learning implicit brain mri manifolds with deep learning"
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"
    - "dropout gan learning from a dynamic ensemble of discriminators"
    - "dual discriminator generative adversarial nets"

Connected through hyperedge HE-2 (induced by source paper "deep learning architectures for generalized immunofluorescence based nuclear image segmentation"):
  with members:
    - "deep adversarial training for multi organ nuclei segmentation in histopathology images"
    - "mask r cnn"
    - "image to image translation with conditional adversarial networks"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "u net convolutional networks for biomedical image segmentation"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"):
  with members:
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "gans for biological image synthesis"
    - "deep generative adversarial networks for compressed sensing automates mri"
    - "depthsynth real time realistic synthetic data generation from cad models for 2 5d recognition"
    - "learning from synthetic humans"
    - "deep learning and conditional random fields based depth estimation and topographical reconstruction from conventional endoscopy"
    - "adversarial training and dilated convolutions for brain mri segmentation"
    - "adversarial networks for the detection of aggressive prostate cancer"

Connected through hyperedge HE-4 (induced by source paper "conditional infilling gans for data augmentation in mammogram classification"):
  with members:
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "on convergence and stability of gans"
    - "low shot learning from imaginary data"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "improved training of wasserstein gans"
    - "generalization of deep neural networks for chest pathology classification in x rays using generative adversarial networks"
    - "a multi scale cnn and curriculum learning strategy for mammogram classification"

Connected through hyperedge HE-5 (induced by source paper "deep adversarial training for multi organ nuclei segmentation in histopathology images"):
  with members:
    - "task driven generative modeling for unsupervised domain adaptation application to x ray image segmentation"
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "the gan landscape losses architectures regularization and normalization"
    - "deep learning with cinematic rendering fine tuning deep neural networks using photorealistic medical images"
    - "rethinking monocular depth estimation with adversarial training"
    - "semantic aware generative adversarial nets for unsupervised domain adaptation in chest x ray segmentation"
    - "a deep learning algorithm for one step contour aware nuclei segmentation of histopathological images"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "latent space optimal transport for generative models"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "began boundary equilibrium generative adversarial networks"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "generative face completion"
    - "a geometric view of optimal transportation and generative model"
    - "creativity generating diverse questions using variational autoencoders"
    - "neural face editing with intrinsic image disentangling"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "chest x ray inpainting with deep generative models"):
  with members:
    - "generative image inpainting with contextual attention"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "semantic image inpainting with deep generative models"
    - "context encoders feature learning by inpainting"
    - "tensorflow a system for large scale machine learning"
    - "u net convolutional networks for biomedical image segmentation"
    - "unsupervised representation learning with deep convolutional 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)