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: "sub cortical brain structure segmentation using f cnn s"
  Abstract: In this paper we propose a deep learning approach for segmenting sub-cortical structures of the human brain in Magnetic Resonance (MR) image data. We draw inspiration from a state-of-the-art Fully-Convolutional Neural Network (F-CNN) architecture for semantic segmentation of objects in natural images, and adapt it to our task. Unlike previous CNN-based methods that operate on image patches, our model is applied on a full blown 2D image, without any alignment or registration steps at testing time. We further improve segmentation results by interpreting the CNN output as potentials of a Markov Random Field (MRF), whose topology corresponds to a volumetric grid. Alpha-expansion is used to perform approximate inference imposing spatial volumetric homogeneity to the CNN priors. We compare the performance of the proposed pipeline with a similar system using Random Forest-based priors, as well as state-of-art segmentation algorithms, and show promising results on two different brain MRI datasets.

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"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"

Connected through hyperedge HE-2 (induced by source paper "developing brain atlas through deep learning"):
  with members:
    - "non rigid image registration using fully convolutional networks with deep self supervision"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "mask r cnn"
    - "a large deformation diffeomorphic approach to registration of clarity images via mutual information"
    - "hough cnn deep learning for segmentation of deep brain regions in mri and ultrasound"
    - "feature pyramid networks for object detection"
    - "deep residual learning for image recognition"
    - "fast r cnn"

Connected through hyperedge HE-3 (induced by source paper "brain segmentation based on multi atlas guided 3d fully convolutional network ensembles"):
  with members:
    - "cnn based segmentation of medical imaging data"
    - "brain tumor segmentation with deep neural networks"
    - "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"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "automatic segmentation of mr brain images with a convolutional neural network"
    - "deep neural networks for anatomical brain segmentation"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "joint learning of brain lesion and anatomy segmentation from heterogeneous datasets"):
  with members:
    - "neuronet fast and robust reproduction of multiple brain image segmentation pipelines"
    - "ensembles of multiple models and architectures for robust brain tumour segmentation"
    - "hemis hetero modal image segmentation"
    - "deep learning for multi task medical image segmentation in multiple modalities"
    - "unsupervised domain adaptation in brain lesion segmentation with adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-5 (induced by source paper "automated sub cortical brain structure segmentation combining spatial and deep convolutional features"):
  with members:
    - "detecting cancer metastases on gigapixel pathology images"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"
    - "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"
    - "hough cnn deep learning for segmentation of deep brain regions in mri and ultrasound"
    - "location sensitive deep convolutional neural networks for segmentation of white matter hyperintensities"
    - "deep residual learning for image recognition"
    - "deep neural networks for anatomical brain segmentation"

Connected through hyperedge HE-6 (induced by source paper "3d fully convolutional networks for subcortical segmentation in mri a large scale study"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "hough cnn deep learning for segmentation of deep brain regions in mri and ultrasound"
    - "automatic segmentation of mr brain images with a convolutional neural network"
    - "semantic image segmentation with task specific edge detection using cnns and a discriminatively trained domain transform"
    - "multi scale context aggregation by dilated convolutions"
    - "deep neural networks for anatomical brain segmentation"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

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)