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: "scargan chained generative adversarial networks to simulate pathological tissue on cardiovascular mr scans"
  Abstract: Medical images with specific pathologies are scarce, but a large amount of data is usually required for a deep convolutional neural network (DCNN) to achieve good accuracy. We consider the problem of segmenting the left ventricular (LV) myocardium on late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) scans of which only some of the scans have scar tissue. We propose ScarGAN to simulate scar tissue on healthy myocardium using chained generative adversarial networks (GAN). Our novel approach factorizes the simulation process into 3 steps: 1) a mask generator to simulate the shape of the scar tissue; 2) a domain-specific heuristic to produce the initial simulated scar tissue from the simulated shape; 3) a refining generator to add details to the simulated scar tissue. Unlike other approaches that generate samples from scratch, we simulate scar tissue on normal scans resulting in highly realistic samples. We show that experienced radiologists are unable to distinguish between real and simulated scar tissue. Training a U-Net with additional scans with scar tissue simulated by ScarGAN increases the percentage of scar pixels correctly included in LV myocardium prediction from 75.9% to 80.5%.

Connected through hyperedge HE-1 (induced by source paper "conditional generative adversarial networks for data augmentation and adaptation in remotely sensed imagery"):
  with members:
    - "multimodal 3d object detection from simulated pretraining"
    - "domain adaptive generation of aircraft on satellite imagery via simulated and unsupervised learning"
    - "xview objects in context in overhead imagery"
    - "pioneer networks progressively growing generative autoencoder"
    - "deep learning in medical image analysis and multimodal learning for clinical decision support 4th international workshop dlmia 2018 and 8th international workshop ml cds 2018 held in conjunction with miccai 2018 granada spain september 20 2018 proceedings"
    - "a style based generator architecture for generative adversarial networks"
    - "structured domain randomization bridging the reality gap by context aware synthetic data"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"

Connected through hyperedge HE-2 (induced by source paper "atrial scar quantification via multi scale cnn in the graph cuts framework"):
  with members:
    - "fully automatic segmentation and objective assessment of atrial scars for long standing persistent atrial fibrillation patients using late gadolinium enhanced mri"
    - "atrial fibrosis quantification based on maximum likelihood estimator of multivariate images"
    - "multiview two task recursive attention model for left atrium and atrial scars segmentation"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "semi supervised classification with graph convolutional networks"
    - "pointnet deep learning on point sets for 3d classification and segmentation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "decompose to manipulate manipulable object synthesis in 3d medical images with structured image decomposition"):
  with members:
    - "on the convergence of adam and beyond"
    - "factorised spatial representation learning application in semi supervised myocardial segmentation"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "conditional infilling gans for data augmentation in mammogram classification"
    - "gp gan gender preserving gan for synthesizing faces from landmarks"
    - "spectral normalization for generative adversarial networks"
    - "learning hierarchical semantic image manipulation through structured representations"
    - "ct realistic lung nodule simulation from 3d conditional generative adversarial networks for robust lung segmentation"

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)