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: "automatic calcium scoring in low dose chest ct using deep neural networks with dilated convolutions"
  Abstract: Heavy smokers undergoing screening with low-dose chest CT are affected by cardiovascular disease as much as by lung cancer. Low-dose chest CT scans acquired in screening enable quantification of atherosclerotic calcifications and thus enable identification of subjects at increased cardiovascular risk. This paper presents a method for automatic detection of coronary artery, thoracic aorta, and cardiac valve calcifications in low-dose chest CT using two consecutive convolutional neural networks. The first network identifies and labels potential calcifications according to their anatomical location and the second network identifies true calcifications among the detected candidates. This method was trained and evaluated on a set of 1744 CT scans from the National Lung Screening Trial. To determine whether any reconstruction or only images reconstructed with soft tissue filters can be used for calcification detection, we evaluated the method on soft and medium/sharp filter reconstructions separately. On soft filter reconstructions, the method achieved F1 scores of 0.89, 0.89, 0.67, and 0.55 for coronary artery, thoracic aorta, aortic valve, and mitral valve calcifications, respectively. On sharp filter reconstructions, the F1 scores were 0.84, 0.81, 0.64, and 0.66, respectively. Linearly weighted kappa coefficients for risk category assignment based on per subject coronary artery calcium were 0.91 and 0.90 for soft and sharp filter reconstructions, respectively. These results demonstrate that the presented method enables reliable automatic cardiovascular risk assessment in all low-dose chest CT scans acquired for lung cancer screening.

Connected through hyperedge HE-1 (induced by source paper "knowledge based analysis for mortality prediction from ct images"):
  with members:
    - "direct automatic coronary calcium scoring in cardiac and chest ct"
    - "hybrid deep neural networks for all cause mortality prediction from ldct images"
    - "direct prediction of cardiovascular mortality from low dose chest ct using deep learning"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "visual saliency based on multiscale deep features"
    - "deep residual learning for image recognition"
    - "learning deep features for discriminative localization"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "deep learning in cardiology"):
  with members:
    - "pixelbnn augmenting the pixelcnn with batch normalization and the presentation of a fast architecture for retinal vessel segmentation"
    - "estimation of the volume of the left ventricle from mri images using deep neural networks"
    - "deep learning analysis of the myocardium in coronary ct angiography for identification of patients with functionally significant coronary artery stenosis"
    - "convolutional capsule network for classification of breast cancer histology images"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "brain tumor type classification via capsule networks"
    - "deepheart semi supervised sequence learning for cardiovascular risk prediction"

Connected through hyperedge HE-3 (induced by source paper "one pass multi task networks with cross task guided attention for brain tumor segmentation"):
  with members:
    - "brain tumor segmentation and radiomics survival prediction contribution to the brats 2017 challenge"
    - "harmonious attention network for person re identification"
    - "adaptive feature recombination and recalibration for semantic segmentation application to brain tumor segmentation in mri"
    - "3d mri brain tumor segmentation using autoencoder regularization"
    - "compnet complementary segmentation network for brain mri extraction"
    - "no new net"
    - "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "sparsely activated networks a new method for decomposing and compressing data"):
  with members:
    - "deep learning analysis of the myocardium in coronary ct angiography for identification of patients with functionally significant coronary artery stenosis"
    - "convolutional capsule network for classification of breast cancer histology images"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "brain tumor type classification via capsule networks"
    - "medical image analysis using convolutional neural networks a review"
    - "beat by beat classifying cardiac arrhythmias with recurrent neural networks"
    - "recognizing abnormal heart sounds using deep learning"
    - "dynamic routing between capsules"

Connected through hyperedge HE-5 (induced by source paper "direct automatic coronary calcium scoring in cardiac and chest ct"):
  with members:
    - "a deep learning framework for unsupervised affine and deformable image registration"
    - "direct and real time cardiovascular risk prediction"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "convnet based localization of anatomical structures in 3 d medical images"
    - "theano a python framework for fast computation of mathematical expressions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "a recurrent cnn for automatic detection and classification of coronary artery plaque and stenosis in coronary ct angiography"):
  with members:
    - "coronary artery centerline extraction in cardiac ct angiography using a cnn based orientation classifier"
    - "a survey on deep learning in medical image analysis"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "beyond short snippets deep networks for video classification"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "deep visual semantic alignments for generating image descriptions"

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)