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: "fully automatic segmentation and objective assessment of atrial scars for long standing persistent atrial fibrillation patients using late gadolinium enhanced mri"
  Abstract: Purpose: Atrial fibrillation (AF) is the most common heart rhythm disorder and causes considerable morbidity and mortality, resulting in a large public health burden that is increasing as the population ages. It is associated with atrial fibrosis, the amount and distribution of which can be used to stratify patients and to guide subsequent electrophysiology ablation treatment. Atrial fibrosis may be assessed non-invasively using late gadolinium-enhanced (LGE) magnetic resonance imaging (MRI) where scar tissue is visualised as a region of signal enhancement. However, manual segmentation of the heart chambers and of the atrial scar tissue is time-consuming and subject to inter-operator variability, particularly as image quality in AF is often poor. In this study, we propose a novel fully automatic pipeline to achieve accurate and objective segmentation of the heart (from MRI Roadmap data) and of scar tissue within the heart (from LGE MRI data) acquired in patients with AF. #R##N#Methods: Our fully automatic pipeline uniquely combines: (1) a multi-atlas based whole heart segmentation (MA-WHS) to determine the cardiac anatomy from an MRI Roadmap acquisition which is then mapped to LGE MRI, and (2) a super-pixel and supervised learning based approach to delineate the distribution and extent of atrial scarring in LGE MRI. We compared the accuracy of the automatic analysis to manual ground-truth segmentations in 37 patients with persistent long standing AF.#R##N#Results: Both our MA-WHS and atrial scarring segmentations showed accurate delineations of cardiac anatomy (mean Dice = 89%) and atrial scarring (mean Dice = 79%) respectively compared to the established ground truth from manual segmentation. In addition, compared to the ground truth, we obtained 88% segmentation accuracy, with 90% sensitivity and 79% specificity. Receiver operating characteristic analysis achieved an average area under the curve of 0.91.#R##N#Conclusion: Compared with previously studied methods with manual interventions, our innovative pipeline demonstrated comparable results, but was computed fully automatically. The proposed segmentation methods allow LGE MRI to be used as an objective assessment tool for localisation, visualisation and quantification of atrial scarring and to guide ablation treatment.

Connected through hyperedge HE-1 (induced by source paper "atrial scar quantification via multi scale cnn in the graph cuts framework"):
  with members:
    - "atrial fibrosis quantification based on maximum likelihood estimator of multivariate images"
    - "scargan chained generative adversarial networks to simulate pathological tissue on cardiovascular mr scans"
    - "multiview two task recursive attention model for left atrium and atrial scars segmentation"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "semi supervised classification with graph convolutional networks"
    - "pointnet deep learning on point sets for 3d classification and segmentation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "discriminative consistent domain generation for semi supervised learning"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "domain invariant adversarial learning for unsupervised domain adaption"
    - "multiview two task recursive attention model for left atrium and atrial scars segmentation"
    - "an adversarial regularisation for semi supervised training of structured output neural networks"
    - "3d densely convolutional networks for volumetric segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-3 (induced by source paper "dilated convolutions in neural networks for left atrial segmentation in 3d gadolinium enhanced mri"):
  with members:
    - "combating uncertainty with novel losses for automatic left atrium segmentation"
    - "pyramid network with online hard example mining for accurate left atrium segmentation"
    - "cardiacnet segmentation of left atrium and proximal pulmonary veins from mri using multi view cnn"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-4 (induced by source paper "multiview two task recursive attention model for left atrium and atrial scars segmentation"):
  with members:
    - "cardiacnet segmentation of left atrium and proximal pulmonary veins from mri using multi view cnn"
    - "residual attention network for image classification"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

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)