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: "infinite brain mr images pggan based data augmentation for tumor detection"
  Abstract: Due to the lack of available annotated medical images, accurate computer-assisted diagnosis requires intensive data augmentation (DA) techniques, such as geometric/intensity transformations of original images; however, those transformed images intrinsically have a similar distribution to the original ones, leading to limited performance improvement. To fill the data lack in the real image distribution, we synthesize brain contrast-enhanced magnetic resonance (MR) images—realistic but completely different from the original ones—using generative adversarial networks (GANs). This study exploits progressive growing of GANs (PGGANs), a multistage generative training method, to generate original-sized \(256\times 256\) MR images for convolutional neural network-based brain tumor detection, which is challenging via conventional GANs; difficulties arise due to unstable GAN training with high resolution and a variety of tumors in size, location, shape, and contrast. Our preliminary results show that this novel PGGAN-based DA method can achieve a promising performance improvement, when combined with classical DA, in tumor detection and also in other medical imaging tasks.

Connected through hyperedge HE-1 (induced by source paper "on leveraging pretrained gans for limited data generation"):
  with members:
    - "transfer learning in biomedical natural language processing an evaluation of bert and elmo on ten benchmarking datasets"
    - "learning more with less conditional pggan based data augmentation for brain metastases detection using highly rough annotation on mr images"
    - "open event extraction from online text using a generative adversarial network"
    - "analyzing and improving the image quality of stylegan"
    - "generative adversarial network in medical imaging a review"
    - "a bert based transfer learning approach for hate speech detection in online social media"
    - "image generation from small datasets via batch statistics adaptation"
    - "mind2mind transfer learning for gans"

Connected through hyperedge HE-2 (induced by source paper "learning more with less conditional pggan based data augmentation for brain metastases detection using highly rough annotation on mr images"):
  with members:
    - "a three player gan generating hard samples to improve classification networks"
    - "red blood cell image generation for data augmentation using conditional generative adversarial networks"
    - "use net incorporating squeeze and excitation blocks into u net for prostate zonal segmentation of multi institutional mri datasets"
    - "generative image translation for data augmentation of bone lesion pathology"
    - "towards generative adversarial networks as a new paradigm for radiology education"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "yolov3 an incremental improvement"
    - "pedestrian synthesis gan generating pedestrian data in real scene and beyond"

Connected through hyperedge HE-3 (induced by source paper "synthesizing diverse lung nodules wherever massively 3d multi conditional gan based ct image augmentation for object detection"):
  with members:
    - "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"
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"

Connected through hyperedge HE-4 (induced by source paper "learning more with less gan based medical image augmentation"):
  with members:
    - "learning more with less conditional pggan based data augmentation for brain metastases detection using highly rough annotation on mr images"
    - "yolov3 an incremental improvement"
    - "pedestrian synthesis gan generating pedestrian data in real scene and beyond"
    - "webly supervised learning for skin lesion classification"
    - "ct realistic lung nodule simulation from 3d conditional generative adversarial networks for robust lung segmentation"
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"
    - "progressive growing of gans for improved quality stability and variation"
    - "improved training of wasserstein gans"

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)