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: "conditional infilling gans for data augmentation in mammogram classification"
  Abstract: Deep learning approaches to breast cancer detection in mammograms have recently shown promising results. However, such models are constrained by the limited size of publicly available mammography datasets, in large part due to privacy concerns and the high cost of generating expert annotations. Limited dataset size is further exacerbated by substantial class imbalance since "normal" images dramatically outnumber those with findings. Given the rapid progress of generative models in synthesizing realistic images, and the known effectiveness of simple data augmentation techniques (e.g. horizontal flipping), we ask if it is possible to synthetically augment mammogram datasets using generative adversarial networks (GANs). We train a class-conditional GAN to perform contextual in-filling, which we then use to synthesize lesions onto healthy screening mammograms. First, we show that GANs are capable of generating high-resolution synthetic mammogram patches. Next, we experimentally evaluate using the augmented dataset to improve breast cancer classification performance. We observe that a ResNet-50 classifier trained with GAN-augmented training data produces a higher AUROC compared to the same model trained only on traditionally augmented data, demonstrating the potential of our approach.

Connected through hyperedge HE-1 (induced by source paper "safe augmentation learning task specific transformations from data"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "fast autoaugment"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "dada deep adversarial data augmentation for extremely low data regime classification"
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "autoaugment learning augmentation policies from data"
    - "data augmentation instead of explicit regularization"

Connected through hyperedge HE-2 (induced by source paper "bias remediation in driver drowsiness detection systems using generative adversarial networks"):
  with members:
    - "bias in bios a case study of semantic representation bias in a high stakes setting"
    - "racial categories in machine learning"
    - "detecting inter sectional accuracy differences in driver drowsiness detection algorithms"
    - "data augmentation for low resource sentiment analysis using generative adversarial networks"
    - "depthwisegans fast training generative adversarial networks for realistic image synthesis"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "text to image synthesis using generative adversarial networks"
    - "domain adaptive transfer learning with specialist models"

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"
    - "scargan chained generative adversarial networks to simulate pathological tissue on cardiovascular mr scans"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "data fine tuning"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "anonymizing k facial attributes via adversarial perturbations"
    - "unravelling robustness of deep learning based face recognition against adversarial attacks"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "data augmentation of wearable sensor data for parkinson s disease monitoring using convolutional neural networks"
    - "mitigating adversarial effects through randomization"
    - "universal adversarial perturbations"

Connected through hyperedge HE-5 (induced by source paper "class aware adversarial lung nodule synthesis in ct images"):
  with members:
    - "decompose to manipulate manipulable object synthesis in 3d medical images with structured image decomposition"
    - "ct realistic lung nodule simulation from 3d conditional generative adversarial networks for robust lung segmentation"
    - "generative image inpainting with contextual attention"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "the kinetics human action video dataset"
    - "improved training of wasserstein gans"
    - "aggregated residual transformations for deep neural networks"
    - "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)