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: "how to fool radiologists with generative adversarial networks a visual turing test for lung cancer diagnosis"
  Abstract: Discriminating lung nodules as malignant or benign is still an underlying challenge. To address this challenge, radiologists need computer aided diagnosis (CAD) systems which can assist in learning discriminative imaging features corresponding to malignant and benign nodules. However, learning highly discriminative imaging features is an open problem. In this paper, our aim is to learn the most discriminative features pertaining to lung nodules by using an adversarial learning methodology. Specifically, we propose to use unsupervised learning with Deep Convolutional-Generative Adversarial Networks (DC-GANs) to generate lung nodule samples realistically. We hypothesize that imaging features of lung nodules will be discriminative if it is hard to differentiate them (fake) from real (true) nodules. To test this hypothesis, we present Visual Turing tests to two radiologists in order to evaluate the quality of the generated (fake) nodules. Extensive comparisons are performed in discerning real, generated, benign, and malignant nodules. This experimental set up allows us to validate the overall quality of the generated nodules, which can then be used to (1) improve diagnostic decisions by mining highly discriminative imaging features, (2) train radiologists for educational purposes, and (3) generate realistic samples to train deep networks with big data.

Connected through hyperedge HE-1 (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"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-2 (induced by source paper "synthesizing diverse lung nodules wherever massively 3d multi conditional gan based ct image augmentation for object detection"):
  with members:
    - "infinite brain mr images pggan based data augmentation for tumor detection"
    - "a three player gan generating hard samples to improve classification networks"
    - "learning more with less conditional pggan based data augmentation for brain metastases detection using highly rough annotation on mr images"
    - "red blood cell image generation for data augmentation using conditional generative adversarial networks"
    - "towards generative adversarial networks as a new paradigm for radiology education"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "mc gan multi conditional generative adversarial network for image synthesis"
    - "pedestrian synthesis gan generating pedestrian data in real scene and beyond"

Connected through hyperedge HE-3 (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-4 (induced by source paper "ct gan malicious tampering of 3d medical imagery using deep learning"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "generative adversarial network in medical imaging a review"
    - "fighting fake news image splice detection via learned self consistency"
    - "melanogans high resolution skin lesion synthesis with gans"
    - "cross modality synthesis from ct to pet using fcn and gan networks for improved automated lesion detection"
    - "forensictransfer weakly supervised domain adaptation for forgery detection"
    - "noiseprint a cnn based camera model fingerprint"
    - "learning implicit brain mri manifolds with deep learning"

Connected through hyperedge HE-5 (induced by source paper "instance level microtubule tracking"):
  with members:
    - "object part attention model for fine grained image classification"
    - "maskrnn instance level video object segmentation"
    - "path aggregation network for instance segmentation"
    - "fine grained discriminative localization via saliency guided faster r cnn"
    - "attention is all you need"
    - "learning to segment every thing"
    - "mask r cnn"
    - "boundary aware instance segmentation"

Connected through hyperedge HE-6 (induced by source paper "lung ct imaging sign classification through deep learning on small data"):
  with members:
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "cvae gan fine grained image generation through asymmetric training"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "deep residual learning for image recognition"
    - "conditional generative adversarial nets"
    - "very deep convolutional networks for large scale image recognition"
    - "semi supervised learning with deep generative models"

Connected through hyperedge HE-7 (induced by source paper "instance level microtubule segmentation using recurrent attention"):
  with members:
    - "maskrnn instance level video object segmentation"
    - "deep geodesic learning for segmentation and anatomical landmarking"
    - "cardiacnet segmentation of left atrium and proximal pulmonary veins from mri using multi view cnn"
    - "end to end instance segmentation with recurrent attention"
    - "learning deconvolution network for semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "concept centric visual turing tests for method validation"):
  with members:
    - "dilated residual networks"
    - "visual question answering a survey of methods and datasets"
    - "vqa visual question answering"
    - "deep residual learning for image recognition"

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)