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: "no new net"
  Abstract: In this paper we demonstrate the effectiveness of a well trained U-Net in the context of the BraTS 2018 challenge. This endeavour is particularly interesting given that researchers are currently besting each other with architectural modifications that are intended to improve the segmentation performance. We instead focus on the training process arguing that a well trained U-Net is hard to beat. Our baseline U-Net, which has only minor modifications and is trained with a large patch size and a Dice loss function indeed achieved competitive Dice scores on the BraTS2018 validation data. By incorporating additional measures such as region based training, additional training data, a simple postprocessing technique and a combination of loss functions, we obtain Dice scores of 77.88, 87.81 and 80.62, and Hausdorff Distances (95th percentile) of 2.90, 6.03 and 5.08 for the enhancing tumor, whole tumor and tumor core, respectively on the test data. This setup achieved rank two in BraTS2018, with more than 60 teams participating in the challenge.

Connected through hyperedge HE-1 (induced by source paper "one pass multi task networks with cross task guided attention for brain tumor segmentation"):
  with members:
    - "brain tumor segmentation and radiomics survival prediction contribution to the brats 2017 challenge"
    - "harmonious attention network for person re identification"
    - "adaptive feature recombination and recalibration for semantic segmentation application to brain tumor segmentation in mri"
    - "3d mri brain tumor segmentation using autoencoder regularization"
    - "compnet complementary segmentation network for brain mri extraction"
    - "automatic calcium scoring in low dose chest ct using deep neural networks with dilated convolutions"
    - "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "multimodal self supervised learning for medical image analysis"):
  with members:
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "domain generalization by solving jigsaw puzzles"
    - "deep learning versus classical regression for brain tumor patient survival prediction"
    - "non rigid image registration using self supervised fully convolutional networks without training data"
    - "deep lesion graphs in the wild relationship learning and organization of significant radiology image findings in a diverse large scale lesion database"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "3d dilated multi fiber network for real time brain tumor segmentation in mri"):
  with members:
    - "multi fiber networks for video recognition"
    - "3d mri brain tumor segmentation using autoencoder regularization"
    - "brain tumor segmentation and tractographic feature extraction from structural mr images for overall survival prediction"
    - "autofocus layer for semantic segmentation"
    - "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "brain tumor segmentation with deep neural networks"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-4 (induced by source paper "improving 3d u net for brain tumor segmentation by utilizing lesion prior"):
  with members:
    - "on the convergence of adam and beyond"
    - "brain tumor segmentation and radiomics survival prediction contribution to the brats 2017 challenge"
    - "group normalization"
    - "3d mri brain tumor segmentation using autoencoder regularization"
    - "brain tumor segmentation and tractographic feature extraction from structural mr images for overall survival prediction"
    - "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"
    - "ensembles of multiple models and architectures for robust brain tumour segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-5 (induced by source paper "semi supervised variational autoencoder for survival prediction"):
  with members:
    - "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"
    - "automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks"
    - "self normalizing neural networks"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "semi supervised learning with deep generative models"

Connected through hyperedge HE-6 (induced by source paper "optimizing the dice score and jaccard index for medical image segmentation theory and practice"):
  with members:
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "tversky loss function for image segmentation using 3d fully convolutional deep networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "multi modal segmentation with missing mr sequences using pre trained fusion networks"):
  with members:
    - "hemis hetero modal image segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "automatic brain tumor segmentation using convolutional neural networks with test time augmentation"):
  with members:
    - "aleatoric uncertainty estimation with test time augmentation for medical image segmentation with convolutional neural networks"
    - "towards safe deep learning accurately quantifying biomarker uncertainty in neural network predictions"
    - "niftynet a deep learning platform for medical imaging"
    - "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"
    - "interactive medical image segmentation using deep learning with image specific fine tuning"
    - "automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks"
    - "brain tumor segmentation with deep neural networks"
    - "ensembles of multiple models and architectures for robust brain tumour 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)