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: "deepnat deep convolutional neural network for segmenting neuroanatomy"
  Abstract: We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstract feature representation and a multi-class classification. We propose a 3D patch-based approach, where we do not only predict the center voxel of the patch but also neighbors, which is formulated as multi-task learning. To address a class imbalance problem, we arrange two networks hierarchically, where the first one separates foreground from background, and the second one identifies 25 brain structures on the foreground. Since patches lack spatial context, we augment them with coordinates. To this end, we introduce a novel intrinsic parameterization of the brain volume, formed by eigenfunctions of the Laplace-Beltrami operator. As network architecture, we use three convolutional layers with pooling, batch normalization, and non-linearities, followed by fully connected layers with dropout. The final segmentation is inferred from the probabilistic output of the network with a 3D fully connected conditional random field, which ensures label agreement between close voxels. The roughly 2.7 million parameters in the network are learned with stochastic gradient descent. Our results show that DeepNAT compares favorably to state-of-the-art methods. Finally, the purely learning-based method may have a high potential for the adaptation to young, old, or diseased brains by fine-tuning the pre-trained network with a small training sample on the target application, where the availability of larger datasets with manual annotations may boost the overall segmentation accuracy in the future.

Connected through hyperedge HE-1 (induced by source paper "deep convolutional neural networks for brain image analysis on magnetic resonance imaging a review"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "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"
    - "generalised wasserstein dice score for imbalanced multi class segmentation using holistic convolutional networks"
    - "3d fully convolutional networks for subcortical segmentation in mri a large scale study"

Connected through hyperedge HE-2 (induced by source paper "accurate automatic segmentation of amygdala subnuclei and modeling of uncertainty via bayesian fully convolutional neural network"):
  with members:
    - "niftynet a deep learning platform for medical imaging"
    - "dilated residual networks"
    - "3d fully convolutional networks for subcortical segmentation in mri a large scale study"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "location sensitive deep convolutional neural networks for segmentation of white matter hyperintensities"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "learnable pooling in graph convolution networks for brain surface analysis"):
  with members:
    - "hierarchical graph representation learning with differentiable pooling"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "local spectral graph convolution for point set feature learning"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"
    - "geometric deep learning going beyond euclidean data"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "3d fully convolutional networks for subcortical segmentation in mri a large scale study"

Connected through hyperedge HE-4 (induced by source paper "feature fused context encoding network for neuroanatomy segmentation"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "context encoding for semantic segmentation"
    - "error corrective boosting for learning fully convolutional networks with limited data"
    - "mask r cnn"
    - "densely connected convolutional networks"
    - "pyramid scene parsing network"
    - "automatic segmentation of mr brain images with a convolutional neural network"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "psacnn pulse sequence adaptive fast whole brain segmentation"):
  with members:
    - "quicknat segmenting mri neuroanatomy in 20 seconds"
    - "3d segmentation with exponential logarithmic loss for highly unbalanced object sizes"
    - "pulse sequence resilient fast brain segmentation"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "unsupervised cross domain image generation"

Connected through hyperedge HE-6 (induced by source paper "3d whole brain segmentation using spatially localized atlas network tiles"):
  with members:
    - "compnet complementary segmentation network for brain mri extraction"
    - "spatially localized atlas network tiles enables 3d whole brain segmentation from limited data"
    - "neuronet fast and robust reproduction of multiple brain image segmentation pipelines"
    - "3d segmentation with exponential logarithmic loss for highly unbalanced object sizes"
    - "4d multi atlas label fusion using longitudinal images"
    - "error corrective boosting for learning fully convolutional networks with limited data"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-7 (induced by source paper "extended 2d volumetric consensus hippocampus segmentation"):
  with members:
    - "brain tumor segmentation and radiomics survival prediction contribution to the brats 2017 challenge"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "near real time hippocampus segmentation using patch based canonical neural network"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "simultaneous lesion and neuroanatomy segmentation in multiple sclerosis using deep neural networks"):
  with members:
    - "one shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks"
    - "neuronet fast and robust reproduction of multiple brain image segmentation pipelines"
    - "focal loss for dense object detection"
    - "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "sgdr stochastic gradient descent with restarts"
    - "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)