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: "2d 3d fully convolutional neural networks for cardiac mr segmentation"
  Abstract: In this paper, we develop a 2D and 3D segmentation pipelines for fully automated cardiac MR image segmentation using Deep Convolutional Neural Networks (CNN). Our models are trained end-to-end from scratch using the ACD Challenge 2017 dataset comprising of 100 studies, each containing Cardiac MR images in End Diastole and End Systole phase. We show that both our segmentation models achieve near state-of-the-art performance scores in terms of distance metrics and have convincing accuracy in terms of clinical parameters. A comparative analysis is provided by introducing a novel dice loss function and its combination with cross entropy loss. By exploring different network structures and comprehensive experiments, we discuss several key insights to obtain optimal model performance, which also is central to the theme of this challenge.

Connected through hyperedge HE-1 (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-2 (induced by source paper "fully convolutional multi scale residual densenets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers"):
  with members:
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "brain tumor segmentation with deep neural networks"
    - "ensembles of multiple models and architectures for robust brain tumour segmentation"
    - "an exploration of 2d and 3d deep learning techniques for cardiac mr image segmentation"
    - "automated cardiovascular magnetic resonance image analysis with fully convolutional networks"
    - "fastventricle cardiac segmentation with enet"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "automatic segmentation and disease classification using cardiac cine mr images"

Connected through hyperedge HE-3 (induced by source paper "accuracy uncertainty and adaptability of automatic myocardial asl segmentation using deep cnn"):
  with members:
    - "aleatoric uncertainty estimation with test time augmentation for medical image segmentation with convolutional neural networks"
    - "a probabilistic u net for segmentation of ambiguous images"
    - "an exploration of 2d and 3d deep learning techniques for cardiac mr image segmentation"
    - "automated cardiovascular magnetic resonance image analysis with fully convolutional networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "deep bayesian active learning with image data"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "automatic 3d bi ventricular segmentation of cardiac images by a shape constrained multi task deep learning approach"):
  with members:
    - "learning based quality control for cardiac mr images"
    - "deep nested level sets fully automated segmentation of cardiac mr images in patients with pulmonary hypertension"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "left ventricle segmentation in cardiac mr images using fully convolutional network"
    - "fully convolutional multi scale residual densenets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers"
    - "cardiacnet segmentation of left atrium and proximal pulmonary veins from mri using multi view cnn"
    - "an exploration of 2d and 3d deep learning techniques for cardiac mr image segmentation"
    - "automatic cardiac disease assessment on cine mri via time series segmentation and domain specific features"

Connected through hyperedge HE-5 (induced by source paper "development and validation of deep learning algorithms for detection of critical findings in head ct scans"):
  with members:
    - "learning to diagnose from scratch by exploiting dependencies among labels"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "radnet radiologist level accuracy using deep learning for hemorrhage detection in ct scans"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "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)