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: "deep cnn ensembles and suggestive annotations for infant brain mri segmentation"
  Abstract: Precise 3D segmentation of infant brain tissues is an essential step towards comprehensive volumetric studies and quantitative analysis of early brain developement. However, computing such segmentations is very challenging, especially for 6-month infant brain, due to the poor image quality, among other difficulties inherent to infant brain MRI, e.g., the isointense contrast between white and gray matter and the severe partial volume effect due to small brain sizes. This study investigates the problem with an ensemble of semi-dense fully convolutional neural networks (CNNs), which employs T1-weighted and T2-weighted MR images as input. We demonstrate that the ensemble agreement is highly correlated with the segmentation errors. Therefore, our method provides measures that can guide local user corrections. To the best of our knowledge, this work is the first ensemble of 3D CNNs for suggesting annotations within images. Furthermore, inspired by the very recent success of dense networks, we propose a novel architecture, SemiDenseNet, which connects all convolutional layers directly to the end of the network. Our architecture allows the efficient propagation of gradients during training, while limiting the number of parameters, requiring one order of magnitude less parameters than popular medical image segmentation networks such as 3D U-Net. Another contribution of our work is the study of the impact that early or late fusions of multiple image modalities might have on the performances of deep architectures. We report evaluations of our method on the public data of the MICCAI iSEG-2017 Challenge on 6-month infant brain MRI segmentation, and show very competitive results among 21 teams, ranking first or second in most metrics.

Connected through hyperedge HE-1 (induced by source paper "multiregion segmentation of bladder cancer structures in mri with progressive dilated convolutional networks"):
  with members:
    - "semantic segmentation of pathological lung tissue with dilated fully convolutional networks"
    - "hyperdense net a hyper densely connected cnn for multi modal image segmentation"
    - "unbiased shape compactness for segmentation"
    - "ensembles of multiple models and architectures for robust brain tumour segmentation"
    - "gridnet with automatic shape prior registration for automatic mri cardiac segmentation"
    - "understanding convolution for semantic segmentation"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-2 (induced by source paper "hyperdense net a hyper densely connected cnn for multi modal image segmentation"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "scalable multimodal convolutional networks for brain tumour segmentation"
    - "h denseunet hybrid densely connected unet for liver and liver tumor segmentation from ct volumes"
    - "multi scale dense networks for resource efficient image classification"
    - "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach"
    - "isointense infant brain segmentation with a hyper dense connected convolutional neural network"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"

Connected through hyperedge HE-3 (induced by source paper "dense multi path u net for ischemic stroke lesion segmentation in multiple image modalities"):
  with members:
    - "multi modal convolutional neural networks for brain tumor segmentation"
    - "hyperdense net a hyper densely connected cnn for multi modal image segmentation"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach"
    - "isointense infant brain segmentation with a hyper dense connected convolutional neural network"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "automatic brain tumor detection and segmentation using u net based fully convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "ivd net intervertebral disc localization and segmentation in mri with a multi modal unet"):
  with members:
    - "dense multi path u net for ischemic stroke lesion segmentation in multiple image modalities"
    - "multi modal convolutional neural networks for brain tumor segmentation"
    - "hyperdense net a hyper densely connected cnn for multi modal image segmentation"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach"
    - "isointense infant brain segmentation with a hyper dense connected convolutional neural network"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"

Connected through hyperedge HE-5 (induced by source paper "cnns fusion for building detection in aerial images for the building detection challenge"):
  with members:
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "ensembles of multiple models and architectures for robust brain tumour segmentation"
    - "recurrent neural networks to correct satellite image classification maps"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "deep watershed transform for instance segmentation"
    - "deep neural ensemble for retinal vessel segmentation in fundus images towards achieving label free angiography"
    - "classification with an edge improving semantic image segmentation with boundary detection"
    - "pyramid scene parsing network"

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)