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 neural networks for anatomical brain segmentation"
  Abstract: We present a novel approach to automatically segment magnetic resonance (MR) images of the human brain into anatomical regions. Our methodology is based on a deep artificial neural network that assigns each voxel in an MR image of the brain to its corresponding anatomical region. The inputs of the network capture information at different scales around the voxel of interest: 3D and orthogonal 2D intensity patches capture the local spatial context while large, compressed 2D orthogonal patches and distances to the regional centroids enforce global spatial consistency. Contrary to commonly used segmentation methods, our technique does not require any non-linear registration of the MR images. To benchmark our model, we used the dataset provided for the MICCAI 2012 challenge on multi-atlas labelling, which consists of 35 manually segmented MR images of the brain. We obtained competitive results (mean dice coefficient 0.725, error rate 0.163) showing the potential of our approach. To our knowledge, our technique is the first to tackle the anatomical segmentation of the whole brain using deep neural networks.

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 "medical image super resolution using a generative adversarial network"):
  with members:
    - "a multi channel network with image retrieval for accurate brain tissue segmentation"
    - "a strategy of mr brain tissue images suggestive annotation based on modified u net"
    - "perceptual losses for real time style transfer and super resolution"
    - "accelerating the super resolution convolutional neural network"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "identity mappings in deep residual networks"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "automatic segmentation of mr brain images with a convolutional neural network"

Connected through hyperedge HE-3 (induced by source paper "a multi path 2 5 dimensional convolutional neural network system for segmenting stroke lesions in brain mri images"):
  with members:
    - "deep convolutional neural networks for brain image analysis on magnetic resonance imaging a review"
    - "3d segmentation with exponential logarithmic loss for highly unbalanced object sizes"
    - "brain tumor segmentation with deep neural networks"
    - "joint sequence learning and cross modality convolution for 3d biomedical segmentation"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "identity mappings in deep residual networks"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

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"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"
    - "mask r cnn"
    - "densely connected convolutional networks"
    - "pyramid scene parsing network"
    - "automatic segmentation of mr brain images with a convolutional neural network"

Connected through hyperedge HE-5 (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"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"

Connected through hyperedge HE-6 (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"
    - "sub cortical brain structure segmentation using f cnn s"
    - "automatic segmentation of mr brain images with a convolutional neural network"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "comparenet anatomical segmentation network with deep non local label fusion"):
  with members:
    - "compnet complementary segmentation network for brain mri extraction"
    - "niftynet a deep learning platform for medical imaging"
    - "neural multi atlas label fusion application to cardiac mr images"
    - "a simple neural network module for relational reasoning"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-8 (induced by source paper "distributed deep learning for robust multi site segmentation of ct imaging after traumatic brain injury"):
  with members:
    - "continuous dice coefficient a method for evaluating probabilistic segmentations"
    - "federated learning strategies for improving communication efficiency"
    - "tbi contusion segmentation from mri using convolutional neural networks"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "network in 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)