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: "learning more with less conditional pggan based data augmentation for brain metastases detection using highly rough annotation on mr images"
  Abstract: Accurate Computer-Assisted Diagnosis, associated with proper data wrangling, can alleviate the risk of overlooking the diagnosis in a clinical environment. Towards this, as a Data Augmentation (DA) technique, Generative Adversarial Networks (GANs) can synthesize additional training data to handle the small/fragmented medical imaging datasets collected from various scanners; those images are realistic but completely different from the original ones, filling the data lack in the real image distribution. However, we cannot easily use them to locate disease areas, considering expert physicians' expensive annotation cost. Therefore, this paper proposes Conditional Progressive Growing of GANs (CPGGANs), incorporating highly-rough bounding box conditions incrementally into PGGANs to place brain metastases at desired positions/sizes on 256 × 256 Magnetic Resonance (MR) images, for Convolutional Neural Network-based tumor detection; this first GAN-based medical DA using automatic bounding box annotation improves the training robustness. The results show that CPGGAN-based DA can boost 10% sensitivity in diagnosis with clinically acceptable additional False Positives. Surprisingly, further tumor realism, achieved with additional normal brain MR images for CPGGAN training, does not contribute to detection performance, while even three physicians cannot accurately distinguish them from the real ones in Visual Turing Test.

Connected through hyperedge HE-1 (induced by source paper "on leveraging pretrained gans for limited data generation"):
  with members:
    - "infinite brain mr images pggan based data augmentation for tumor detection"
    - "transfer learning in biomedical natural language processing an evaluation of bert and elmo on ten benchmarking datasets"
    - "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 "bridging the gap between ai and healthcare sides towards developing clinically relevant ai powered diagnosis systems"):
  with members:
    - "enforcing constraints for interpolation and extrapolation in generative adversarial networks"
    - "learning more with less gan based medical image augmentation"
    - "use net incorporating squeeze and excitation blocks into u net for prostate zonal segmentation of multi institutional mri datasets"
    - "coco gan generation by parts via conditional coordinating"
    - "lung nodule classification using deep local global networks"
    - "synthesizing diverse lung nodules wherever massively 3d multi conditional gan based ct image augmentation for object detection"
    - "adaptive weighting multi field of view cnn for semantic segmentation in pathology"
    - "towards generative adversarial networks as a new paradigm for radiology education"

Connected through hyperedge HE-3 (induced by source paper "red blood cell image generation for data augmentation using conditional generative adversarial networks"):
  with members:
    - "medgan medical image translation using gans"
    - "generative adversarial network in medical imaging a review"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "deep adversarial context aware landmark detection for ultrasound imaging"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "high resolution medical image synthesis using progressively grown generative adversarial networks"
    - "btrfly net vertebrae labelling with energy based adversarial learning of local spine prior"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "learning more with less gan based medical image augmentation"):
  with members:
    - "infinite brain mr images pggan based data augmentation for tumor detection"
    - "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)