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 multi scale residual densenets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers"
  Abstract: Deep fully convolutional neural network (FCN) based architectures have shown great potential in medical image segmentation. However, such architectures usually have millions of parameters and inadequate number of training samples leading to over-fitting and poor generalization. In this paper, we present a novel highly parameter and memory efficient FCN based architecture for medical image analysis. We propose a novel up-sampling path which incorporates long skip and short-cut connections to overcome the feature map explosion in FCN like architectures. In order to processes the input images at multiple scales and view points simultaneously, we propose to incorporate Inception module's parallel structures. We also propose a novel dual loss function whose weighting scheme allows to combine advantages of cross-entropy and dice loss. We have validated our proposed network architecture on two publicly available datasets, namely: (i) Automated Cardiac Disease Diagnosis Challenge (ACDC-2017), (ii) Left Ventricular Segmentation Challenge (LV-2011). Our approach in ACDC-2017 challenge stands second place for segmentation and first place in automated cardiac disease diagnosis tasks with an accuracy of 100%. In the LV-2011 challenge our approach attained 0.74 Jaccard index, which is so far the highest published result in fully automated algorithms. From the segmentation we extracted clinically relevant cardiac parameters and hand-crafted features which reflected the clinical diagnostic analysis to train an ensemble system for cardiac disease classification. Our approach combined both cardiac segmentation and disease diagnosis into a fully automated framework which is computational efficient and hence has the potential to be incorporated in computer-aided diagnosis (CAD) tools for clinical application.

Connected through hyperedge HE-1 (induced by source paper "rethinking fully convolutional networks for the analysis of photoluminescence wafer images"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "effective use of dilated convolutions for segmenting small object instances in remote sensing imagery"
    - "rethinking atrous convolution for semantic image segmentation"
    - "full resolution residual networks for semantic segmentation in street scenes"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "densely connected convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "segmentation of roots in soil with u net"):
  with members:
    - "squeeze excite guided few shot segmentation of volumetric images"
    - "flow based self supervised pixel embedding for image segmentation"
    - "on compressing u net using knowledge distillation"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "cnn based segmentation of medical imaging data"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "multi vision attention networks for on line red jujube grading"):
  with members:
    - "on the convergence of adam and beyond"
    - "convolutional neural networks with alternately updated clique"
    - "deep learning in agriculture a survey"
    - "dual skipping networks"
    - "learning transferable architectures for scalable image recognition"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "squeeze and excitation networks"
    - "condensenet an efficient densenet using learned group convolutions"

Connected through hyperedge HE-4 (induced by source paper "unsupervised shape and motion analysis of 3822 cardiac 4d mris of uk biobank"):
  with members:
    - "unsupervised segmentation of 3d medical images based on clustering and deep representation learning"
    - "explainable cardiac pathology classification on cine mri with motion characterization by semi supervised learning of apparent flow"
    - "machine learning methods for histopathological image analysis"
    - "3d consistent biventricular myocardial segmentation using deep learning for mesh generation"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-5 (induced by source paper "attention u net learning where to look for the pancreas"):
  with members:
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "ternarynet faster deep model inference without gpus for medical 3d segmentation using sparse and binary convolutions"
    - "learn to pay attention"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "non local neural networks"
    - "automated cardiovascular magnetic resonance image analysis with fully convolutional networks"
    - "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function"
    - "squeeze and excitation networks"

Connected through hyperedge HE-6 (induced by source paper "explainable cardiac pathology classification on cine mri with motion characterization by semi supervised learning of apparent flow"):
  with members:
    - "learning a probabilistic model for diffeomorphic registration"
    - "voxelmorph a learning framework for deformable medical image registration"
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "enhancing label driven deep deformable image registration with local distance metrics for state of the art cardiac motion tracking"
    - "left ventricle segmentation via optical flow net from short axis cine mri preserving the temporal coherence of cardiac motion"
    - "joint learning of motion estimation and segmentation for cardiac mr image sequences"
    - "learning based regularization for cardiac strain analysis with ability for domain adaptation"
    - "unsupervised probabilistic deformation modeling for robust diffeomorphic registration"

Connected through hyperedge HE-7 (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"
    - "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"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion 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)