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 convolutional network ensembles for white matter hyperintensities segmentation in mr images"
  Abstract: Abstract   White matter hyperintensities (WMH) are commonly found in the brains of healthy elderly individuals and have been associated with various neurological and geriatric disorders. In this paper, we present a study using deep fully convolutional network and ensemble models to automatically detect such WMH using fluid attenuation inversion recovery (FLAIR) and T1 magnetic resonance (MR) scans. The algorithm was evaluated and ranked 1st in the WMH Segmentation Challenge at MICCAI 2017. In the evaluation stage, the implementation of the algorithm was submitted to the challenge organizers, who then independently tested it on a hidden set of 110 cases from 5 scanners. Averaged dice score, precision and robust Hausdorff distance obtained on held-out test datasets were 80%, 84% and 6.30 mm respectively. These were the highest achieved in the challenge, suggesting the proposed method is the state-of-the-art. Detailed descriptions and quantitative analysis on key components of the system were provided. Furthermore, a study of cross-scanner evaluation is presented to discuss how the combination of modalities affect the generalization capability of the system. The adaptability of the system to different scanners and protocols is also investigated. A quantitative study is further presented to show the effect of ensemble size and the effectiveness of the ensemble model. Additionally, software and models of our method are made publicly available. The effectiveness and generalization capability of the proposed system show its potential for real-world clinical practice.

Connected through hyperedge HE-1 (induced by source paper "e uda efficient unsupervised domain adaptation for cross site medical image segmentation"):
  with members:
    - "evaluation of algorithms for multi modality whole heart segmentation an open access grand challenge"
    - "synergistic image and feature adaptation towards cross modality domain adaptation for medical image segmentation"
    - "standardized assessment of automatic segmentation of white matter hyperintensities and results of the wmh segmentation challenge"
    - "boundary aware feature propagation for scene segmentation"
    - "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"
    - "unsupervised cross modality domain adaptation of convnets for biomedical image segmentations with adversarial loss"
    - "cycada cycle consistent adversarial domain adaptation"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "bridging the gap between ai and healthcare sides towards developing clinically relevant ai powered diagnosis systems"):
  with members:
    - "learning more with less conditional pggan based data augmentation for brain metastases detection using highly rough annotation on mr images"
    - "enforcing constraints for interpolation and extrapolation in generative adversarial networks"
    - "learning more with less gan based medical image augmentation"
    - "use net incorporating squeeze and excitation blocks into u net for prostate zonal segmentation of multi institutional mri datasets"
    - "coco gan generation by parts via conditional coordinating"
    - "lung nodule classification using deep local global networks"
    - "synthesizing diverse lung nodules wherever massively 3d multi conditional gan based ct image augmentation for object detection"
    - "adaptive weighting multi field of view cnn for semantic segmentation in pathology"

Connected through hyperedge HE-3 (induced by source paper "standardized assessment of automatic segmentation of white matter hyperintensities and results of the wmh segmentation challenge"):
  with members:
    - "do cifar 10 classifiers generalize to cifar 10"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach"
    - "a survey on deep learning in medical image analysis"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "deep retinal image understanding"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "multi task attention based semi supervised learning for medical image segmentation"):
  with members:
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "3d mri brain tumor segmentation using autoencoder regularization"
    - "3d semi supervised learning with uncertainty aware multi view co training"
    - "attention gated networks learning to leverage salient regions in medical images"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "semi supervised learning with ladder networks"

Connected through hyperedge HE-5 (induced by source paper "ensemble of multi sized fcns to improve white matter lesion segmentation"):
  with members:
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "generalised wasserstein dice score for imbalanced multi class segmentation using holistic convolutional networks"
    - "the importance of skip connections in biomedical image segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "automatic brain structures segmentation using deep residual dilated u net"):
  with members:
    - "multi scale convolutional stack aggregation for robust white matter hyperintensities segmentation"
    - "drunet a dilated residual u net deep learning network to digitally stain optic nerve head tissues in optical coherence tomography images"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-7 (induced by source paper "a post processing method to improve the white matter hyperintensity segmentation accuracy for randomly initialized u net"):
  with members:
    - "white matter hyperintensity segmentation from t1 and flair images using fully convolutional neural networks enhanced with residual connections"
    - "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach"
    - "a survey on deep learning in medical image analysis"
    - "location sensitive deep convolutional neural networks for segmentation of white matter hyperintensities"

Connected through hyperedge HE-8 (induced by source paper "multi scale convolutional stack aggregation for robust white matter hyperintensities segmentation"):
  with members:
    - "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"
    - "fully convolutional networks for semantic 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)