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: "auto context convolutional neural network auto net for brain extraction in magnetic resonance imaging"
  Abstract: Brain extraction or whole brain segmentation is an important first step in many of the neuroimage analysis pipelines. The accuracy and robustness of brain extraction, therefore, is crucial for the accuracy of the entire brain analysis process. With the aim of designing a learning-based, geometry-independent and registration-free brain extraction tool in this study, we present a technique based on an auto-context convolutional neural network (CNN), in which intrinsic local and global image features are learned through 2D patches of different window sizes. In this architecture three parallel 2D convolutional pathways for three different directions (axial, coronal, and sagittal) implicitly learn 3D image information without the need for computationally expensive 3D convolutions. Posterior probability maps generated by the network are used iteratively as context information along with the original image patches to learn the local shape and connectedness of the brain, to extract it from non-brain tissue. #R##N#The brain extraction results we have obtained from our algorithm are superior to the recently reported results in the literature on two publicly available benchmark datasets, namely LPBA40 and OASIS, in which we obtained Dice overlap coefficients of 97.42% and 95.40%, respectively. Furthermore, we evaluated the performance of our algorithm in the challenging problem of extracting arbitrarily-oriented fetal brains in reconstructed fetal brain magnetic resonance imaging (MRI) datasets. In this application our algorithm performed much better than the other methods (Dice coefficient: 95.98%), where the other methods performed poorly due to the non-standard orientation and geometry of the fetal brain in MRI. Our CNN-based method can provide accurate, geometry-independent brain extraction in challenging applications.

Connected through hyperedge HE-1 (induced by source paper "abdominal multi organ segmentation with cascaded convolutional and adversarial deep networks"):
  with members:
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network"
    - "a multi scale pyramid of 3d fully convolutional networks for abdominal multi organ segmentation"
    - "unet a nested u net architecture for medical image segmentation"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "hierarchical 3d fully convolutional networks for multi organ segmentation"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-2 (induced by source paper "liver segmentation in abdominal ct images via auto context neural network and self supervised contour attention"):
  with members:
    - "on the variance of the adaptive learning rate and beyond"
    - "deeply self supervising edge to contour neural network applied to liver segmentation"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "attention gated networks learning to leverage salient regions in medical images"
    - "brain tumor segmentation with deep neural networks"
    - "focal loss for dense object detection"
    - "squeeze and excitation networks"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"

Connected through hyperedge HE-3 (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"
    - "data distillation towards omni supervised learning"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"

Connected through hyperedge HE-4 (induced by source paper "automated brain extraction of multisequence mri using artificial neural networks"):
  with members:
    - "brain tumor segmentation and radiomics survival prediction contribution to the brats 2017 challenge"
    - "deepneuro an open source deep learning toolbox for neuroimaging"
    - "compnet complementary segmentation network for brain mri extraction"
    - "brain extraction from normal and pathological images a joint pca image reconstruction approach"
    - "data augmentation instead of explicit regularization"
    - "imperfect segmentation labels how much do they matter"
    - "cnn based segmentation of medical imaging data"
    - "brain tumor segmentation with deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "deep smoke segmentation"):
  with members:
    - "a cnn rnn architecture for multi label weather recognition"
    - "deeply supervised salient object detection with short connections"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "video salient object detection via fully convolutional networks"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "lstm fully convolutional networks for time series classification"
    - "going deeper with contextual cnn for hyperspectral image classification"

Connected through hyperedge HE-6 (induced by source paper "auto context r cnn"):
  with members:
    - "adversarial patch"
    - "non local neural networks"
    - "deformable convolutional networks"
    - "megdet a large mini batch object detector"
    - "adversarial examples for semantic segmentation and object detection"
    - "adversarial examples that fool detectors"
    - "accurate single stage detector using recurrent rolling convolution"
    - "a unified multi scale deep convolutional neural network for fast object detection"

Connected through hyperedge HE-7 (induced by source paper "real time deep pose estimation with geodesic loss for image to template rigid registration"):
  with members:
    - "real time automatic fetal brain extraction in fetal mri by deep learning"
    - "3d pose regression using convolutional neural networks"
    - "predicting slice to volume transformation in presence of arbitrary subject motion"
    - "slice to volume medical image registration a survey"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a survey on deep learning in medical image analysis"
    - "quicksilver fast predictive image registration a deep learning approach"
    - "6 dof object pose from semantic keypoints"

Connected through hyperedge HE-8 (induced by source paper "segmentation of histological images and fibrosis identification with a convolutional neural network"):
  with members:
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "gland segmentation in colon histology images the glas challenge contest"
    - "a fully convolutional neural network for cardiac segmentation in short axis mri"
    - "segnet a deep convolutional encoder decoder architecture for robust semantic pixel wise labelling"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)