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: "unsupervised domain adaptation for automatic estimation of cardiothoracic ratio"
  Abstract: The cardiothoracic ratio (CTR), a clinical metric of heart size in chest X-rays (CXRs), is a key indicator of cardiomegaly. Manual measurement of CTR is time-consuming and can be affected by human subjectivity, making it desirable to design computer-aided systems that assist clinicians in the diagnosis process. Automatic CTR estimation through chest organ segmentation, however, requires large amounts of pixel-level annotated data, which is often unavailable. To alleviate this problem, we propose an unsupervised domain adaptation framework based on adversarial networks. The framework learns domain invariant feature representations from openly available data sources to produce accurate chest organ segmentation for unlabeled datasets. Specifically, we propose a model that enforces our intuition that prediction masks should be domain independent. Hence, we introduce a discriminator that distinguishes segmentation predictions from ground truth masks. We evaluate our system's prediction based on the assessment of radiologists and demonstrate the clinical practicability for the diagnosis of cardiomegaly. We finally illustrate on the JSRT dataset that the semi-supervised performance of our model is also very promising.

Connected through hyperedge HE-1 (induced by source paper "a survey on deep learning of small sample in biomedical image analysis"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "synergistic image and feature adaptation towards cross modality domain adaptation for medical image segmentation"
    - "generative adversarial network in medical imaging a review"
    - "nnu net breaking the spell on successful medical image segmentation"
    - "constrained cnn losses for weakly supervised segmentation"
    - "angle closure detection in anterior segment oct based on multilevel deep network"
    - "chestnet a deep neural network for classification of thoracic diseases on chest radiography"
    - "boxnet deep learning based biomedical image segmentation using boxes only annotation"

Connected through hyperedge HE-2 (induced by source paper "transformation consistent self ensembling model for semi supervised medical image segmentation"):
  with members:
    - "the liver tumor segmentation benchmark lits"
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "adversarial learning for semi supervised semantic segmentation"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"
    - "discriminative localization in cnns for weakly supervised segmentation of pulmonary nodules"
    - "isic 2017 skin lesion analysis towards melanoma detection"
    - "segmentation of optic disc fovea and retinal vasculature using a single convolutional neural network"
    - "weakly supervised localization of diabetic retinopathy lesions in retinal fundus images"

Connected through hyperedge HE-3 (induced by source paper "curriculum semi supervised segmentation"):
  with members:
    - "constrained cnn losses for weakly supervised segmentation"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "few shot 3d multi modal medical image segmentation using generative adversarial learning"
    - "semi supervised deep learning for fully convolutional networks"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-4 (induced by source paper "uncertainty aware self ensembling model for semi supervised 3d left atrium segmentation"):
  with members:
    - "semi supervised brain lesion segmentation with an adapted mean teacher model"
    - "factorised spatial representation learning application in semi supervised myocardial segmentation"
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "semi supervised skin lesion segmentation via transformation consistent self ensembling model"
    - "deep semi supervised segmentation with weight averaged consistency targets"
    - "multi task learning for left atrial segmentation on ge mri"
    - "semi supervised deep learning for fully convolutional networks"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"

Connected through hyperedge HE-5 (induced by source paper "uncertainty guided domain alignment for layer segmentation in oct images"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation"
    - "joint segmentation and uncertainty visualization of retinal layers in optical coherence tomography images using bayesian deep learning"
    - "a probabilistic u net for segmentation of ambiguous images"
    - "unsupervised cross modality domain adaptation of convnets for biomedical image segmentations with adversarial loss"
    - "adversarial domain adaptation for classification of prostate histopathology whole slide images"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"

Connected through hyperedge HE-6 (induced by source paper "neural style transfer improves 3d cardiovascular mr image segmentation on inconsistent data"):
  with members:
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "a new ensemble learning framework for 3d biomedical image segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "bridging the gap between 2d and 3d organ segmentation with volumetric fusion net"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "dilated convolutional neural networks for cardiovascular mr segmentation in congenital heart disease"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-7 (induced by source paper "uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps"):
  with members:
    - "bts dsn deeply supervised neural network with short connections for retinal vessel segmentation"
    - "3d regression neural network for the quantification of enlarged perivascular spaces in brain mri"
    - "understanding autoencoders with information theoretic concepts"
    - "variable generalization performance of a deep learning model to detect pneumonia in chest radiographs a cross sectional study"
    - "brain tumor segmentation with deep neural networks"
    - "cell detection in microscopy images with deep convolutional neural network and compressed sensing"
    - "opening the black box of deep neural networks via information"
    - "what uncertainties do we need in bayesian deep learning for computer vision"

Connected through hyperedge HE-8 (induced by source paper "3d semi supervised learning with uncertainty aware multi view co training"):
  with members:
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "deep co training for semi supervised image recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "3d anisotropic hybrid network transferring convolutional features from 2d images to 3d anisotropic volumes"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "rethinking atrous convolution for semantic 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)