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: "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
  Abstract: Deep convolutional neural networks are powerful tools for learning visual representations from images. However, designing efficient deep architectures to analyse volumetric medical images remains challenging. This work investigates efficient and flexible elements of modern convolutional networks such as dilated convolution and residual connection. With these essential building blocks, we propose a high-resolution, compact convolutional network for volumetric image segmentation. To illustrate its efficiency of learning 3D representation from large-scale image data, the proposed network is validated with the challenging task of parcellating 155 neuroanatomical structures from brain MR images. Our experiments show that the proposed network architecture compares favourably with state-of-the-art volumetric segmentation networks while being an order of magnitude more compact. We consider the brain parcellation task as a pretext task for volumetric image segmentation; our trained network potentially provides a good starting point for transfer learning. Additionally, we show the feasibility of voxel-level uncertainty estimation using a sampling approximation through dropout.

Connected through hyperedge HE-1 (induced by source paper "mri super resolution with gan and 3d multi level densenet smaller faster and better"):
  with members:
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "brain mri super resolution using 3d deep densely connected neural networks"
    - "learning a mixture of deep networks for single image super resolution"
    - "enhanced deep residual networks for single image super resolution"
    - "improved training of wasserstein gans"
    - "a survey on deep learning in medical image analysis"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-2 (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"
    - "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"
    - "3d fully convolutional networks for subcortical segmentation in mri a large scale study"

Connected through hyperedge HE-3 (induced by source paper "deepigeos a deep interactive geodesic framework for medical image segmentation"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "a survey on deep learning in medical image analysis"
    - "end to end tracking and semantic segmentation using recurrent neural networks"
    - "instance sensitive fully convolutional networks"
    - "temporal convolutional networks for action segmentation and detection"
    - "deepcut object segmentation from bounding box annotations using convolutional neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-4 (induced by source paper "stochastic filter groups for multi task cnns learning specialist and generalist convolution kernels"):
  with members:
    - "evolutionary architecture search for deep multitask networks"
    - "taskonomy disentangling task transfer learning"
    - "uncertainty in multitask learning joint representations for probabilistic mr only radiotherapy planning"
    - "nnu net self adapting framework for u net based medical image segmentation"
    - "niftynet a deep learning platform for medical imaging"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "age progression regression by conditional adversarial autoencoder"
    - "condensenet an efficient densenet using learned group convolutions"

Connected through hyperedge HE-5 (induced by source paper "aleatoric uncertainty estimation with test time augmentation for medical image segmentation with convolutional neural networks"):
  with members:
    - "inherent brain segmentation quality control from fully convnet monte carlo sampling"
    - "exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation"
    - "niftynet a deep learning platform for medical imaging"
    - "interactive medical image segmentation using deep learning with image specific fine tuning"
    - "real time automatic fetal brain extraction in fetal mri by deep learning"
    - "automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks"
    - "auto context convolutional neural network auto net for brain extraction in magnetic resonance imaging"
    - "data distillation towards omni supervised learning"

Connected through hyperedge HE-6 (induced by source paper "standardized assessment of automatic segmentation of white matter hyperintensities and results of the wmh segmentation challenge"):
  with members:
    - "do cifar 10 classifiers generalize to cifar 10"
    - "fully convolutional network ensembles for white matter hyperintensities segmentation in mr images"
    - "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"
    - "rethinking atrous convolution for semantic image segmentation"
    - "deep retinal image understanding"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-7 (induced by source paper "iteratively refined interactive 3d medical image segmentation with multi agent reinforcement learning"):
  with members:
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "fully convolutional network with multi step reinforcement learning for image processing"
    - "unet a nested u net architecture for medical image segmentation"
    - "interactive medical image segmentation using deep learning with image specific fine tuning"
    - "iterative interaction training for segmentation editing networks"
    - "annotating object instances with a polygon rnn"
    - "deepcut object segmentation from bounding box annotations using convolutional neural networks"

Connected through hyperedge HE-8 (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"
    - "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"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"
    - "unsupervised cross domain image generation"

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)