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: "scan structure correcting adversarial network for organ segmentation in chest x rays"
  Abstract: Chest X-ray (CXR) is one of the most commonly prescribed medical imaging procedures, often with over 2-10x more scans than other imaging modalities such as MRI, CT scan, and PET scans. These voluminous CXR scans place significant workloads on radiologists and medical practitioners. Organ segmentation is a crucial step to obtain effective computer-aided detection on CXR. In this work, we propose Structure Correcting Adversarial Network (SCAN) to segment lung fields and the heart in CXR images. SCAN incorporates a critic network to impose on the convolutional segmentation network the structural regularities emerging from human physiology. During training, the critic network learns to discriminate between the ground truth organ annotations from the masks synthesized by the segmentation network. Through this adversarial process the critic network learns the higher order structures and guides the segmentation model to achieve realistic segmentation outcomes. Extensive experiments show that our method produces highly accurate and natural segmentation. Using only very limited training data available, our model reaches human-level performance without relying on any existing trained model or dataset. Our method also generalizes well to CXR images from a different patient population and disease profiles, surpassing the current state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "automatic catheter detection in pediatric x ray images using a scale recurrent network and synthetic data"):
  with members:
    - "scale recurrent network for deep image deblurring"
    - "sharpness aware low dose ct denoising using conditional generative adversarial network"
    - "fully automatic and real time catheter segmentation in x ray fluoroscopy"
    - "deep residual learning for image recognition"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "fully convolutional networks for semantic segmentation"
    - "retinal vessel segmentation using the 2 d gabor wavelet and supervised classification"

Connected through hyperedge HE-2 (induced by source paper "boosting segmentation with weak supervision from image to image translation"):
  with members:
    - "on the convergence of adam and beyond"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "unsupervised detection of lesions in brain mri using constrained adversarial auto encoders"
    - "multimodal unsupervised image to image translation"
    - "3d mri brain tumor segmentation using autoencoder regularization"
    - "spectral normalization for generative adversarial networks"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-3 (induced by source paper "i bet you are wrong gambling adversarial networks for structured semantic segmentation"):
  with members:
    - "understanding deep learning techniques for image segmentation"
    - "adversarial structure matching loss for image segmentation"
    - "star shape prior in fully convolutional networks for skin lesion segmentation"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "student beats the teacher deep neural networks for lateral ventricles segmentation in brain mr"
    - "efficient relaxations for dense crfs with sparse higher order potentials"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "adversarial learning for semi supervised semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "xlsor a robust and accurate lung segmentor on chest x rays using criss cross attention and customized radiorealistic abnormalities generation"):
  with members:
    - "fine grained lesion annotation in ct images with knowledge mined from radiology reports"
    - "abnormal chest x ray identification with generative adversarial one class classifier"
    - "uldor a universal lesion detector for ct scans with pseudo masks and hard negative example mining"
    - "ct image enhancement using stacked generative adversarial networks and transfer learning for lesion segmentation improvement"
    - "multimodal unsupervised image to image translation"
    - "ccnet criss cross attention for semantic segmentation"
    - "attention guided curriculum learning for weakly supervised classification and localization of thoracic diseases on chest radiographs"
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"

Connected through hyperedge HE-5 (induced by source paper "macro micro adversarial network for human parsing"):
  with members:
    - "self produced guidance for weakly supervised object localization"
    - "adversarial complementary learning for weakly supervised object localization"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "adversarial learning for semi supervised semantic segmentation"
    - "holistic instance level human parsing"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "progressive growing of gans for improved quality stability and variation"
    - "camera style adaptation for person re identification"

Connected through hyperedge HE-6 (induced by source paper "el gan embedding loss driven generative adversarial networks for lane detection"):
  with members:
    - "adversarial structure matching loss for image segmentation"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "towards end to end lane detection an instance segmentation approach"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "vpgnet vanishing point guided network for lane and road marking detection and recognition"
    - "splenomegaly segmentation using global convolutional kernels and conditional generative adversarial networks"
    - "adversarial training and dilated convolutions for brain mri segmentation"
    - "adversarial networks for the detection of aggressive prostate cancer"

Connected through hyperedge HE-7 (induced by source paper "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"):
  with members:
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "towards adversarial retinal image synthesis"
    - "virtual pet images from ct data using deep convolutional networks initial results"
    - "a survey on deep learning in medical image analysis"
    - "medical image synthesis with context aware generative adversarial networks"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"

Connected through hyperedge HE-8 (induced by source paper "a generic approach to lung field segmentation from chest radiographs using deep space and shape learning"):
  with members:
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "dcan deep contour aware networks for accurate gland segmentation"
    - "u net convolutional networks for biomedical image 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)