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: "niftynet a deep learning platform for medical imaging"
  Abstract: Abstract   Background and objectives  Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this domain of application requires substantial implementation effort. Consequently, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon.    Methods  The NiftyNet infrastructure provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on the TensorFlow framework and supports features such as TensorBoard visualization of 2D and 3D images and computational graphs by default.    Results  We present three illustrative medical image analysis applications built using NiftyNet infrastructure: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses.    Conclusions  The NiftyNet infrastructure enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.

Connected through hyperedge HE-1 (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"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "age progression regression by conditional adversarial autoencoder"
    - "condensenet an efficient densenet using learned group convolutions"

Connected through hyperedge HE-2 (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"
    - "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"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"

Connected through hyperedge HE-3 (induced by source paper "accurate automatic segmentation of amygdala subnuclei and modeling of uncertainty via bayesian fully convolutional neural network"):
  with members:
    - "dilated residual networks"
    - "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"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "location sensitive deep convolutional neural networks for segmentation of white matter hyperintensities"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "sgd with hardness weighted sampling for distributionally robust deep learning"):
  with members:
    - "an improved analysis of training over parameterized deep neural networks"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "on the convergence rate of training recurrent neural networks"
    - "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"
    - "a convergence theory for deep learning via over parameterization"
    - "training region based object detectors with online hard example mining"
    - "wide residual networks"
    - "minimizing the maximal loss how and why"

Connected through hyperedge HE-5 (induced by source paper "differential privacy enabled federated learning for sensitive health data"):
  with members:
    - "anonymizing data for privacy preserving federated learning"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "adaptive federated learning in resource constrained edge computing systems"
    - "how to backdoor federated learning"
    - "differentially private federated learning a client level perspective"
    - "communication efficient learning of deep networks from decentralized data"
    - "differential privacy and machine learning a survey and review"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-6 (induced by source paper "comparenet anatomical segmentation network with deep non local label fusion"):
  with members:
    - "compnet complementary segmentation network for brain mri extraction"
    - "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"
    - "deep neural networks for anatomical brain segmentation"

Connected through hyperedge HE-7 (induced by source paper "fast infant mri skullstripping with multiview 2d convolutional neural networks"):
  with members:
    - "psacnn pulse sequence adaptive fast whole brain segmentation"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deeporgan multi level deep convolutional networks for automated pancreas segmentation"
    - "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"
    - "no new net"
    - "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)