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: "cnn based segmentation of medical imaging data"
  Abstract: Convolutional neural networks have been applied to a wide variety of computer vision tasks. Recent advances in semantic segmentation have enabled their application to medical image segmentation. While most CNNs use two-dimensional kernels, recent CNN-based publications on medical image segmentation featured three-dimensional kernels, allowing full access to the three-dimensional structure of medical images. Though closely related to semantic segmentation, medical image segmentation includes specific challenges that need to be addressed, such as the scarcity of labelled data, the high class imbalance found in the ground truth and the high memory demand of three-dimensional images. In this work, a CNN-based method with three-dimensional filters is demonstrated and applied to hand and brain MRI. Two modifications to an existing CNN architecture are discussed, along with methods on addressing the aforementioned challenges. While most of the existing literature on medical image segmentation focuses on soft tissue and the major organs, this work is validated on data both from the central nervous system as well as the bones of the hand.

Connected through hyperedge HE-1 (induced by source paper "low memory neural network training a technical report"):
  with members:
    - "traditional and heavy tailed self regularization in neural network models"
    - "pay less attention with lightweight and dynamic convolutions"
    - "learning fast algorithms for linear transforms using butterfly factorizations"
    - "trained quantization thresholds for accurate and efficient fixed point inference of deep neural networks"
    - "memory efficient adaptive optimization"
    - "sparse evolutionary deep learning with over one million artificial neurons on commodity hardware"
    - "fixup initialization residual learning without normalization"
    - "full deep neural network training on a pruned weight budget"

Connected through hyperedge HE-2 (induced by source paper "feature learning to automatically assess radiographic knee osteoarthritis severity"):
  with members:
    - "automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks"
    - "brain tumor segmentation with deep neural networks"
    - "automatic knee osteoarthritis diagnosis from plain radiographs a deep learning based approach"
    - "automatic detection of knee joints and quantification of knee osteoarthritis severity using convolutional neural networks"
    - "a novel method for automatic localization of joint area on knee plain radiographs"
    - "a survey on deep learning in medical image analysis"
    - "deep convolutional networks for automated detection of posterior element fractures on spine ct"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "segmentation of roots in soil with u net"):
  with members:
    - "squeeze excite guided few shot segmentation of volumetric images"
    - "flow based self supervised pixel embedding for image segmentation"
    - "on compressing u net using knowledge distillation"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "fully convolutional multi scale residual densenets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "fast enhanced ct metal artifact reduction using data domain deep learning"):
  with members:
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "deep learning for photoacoustic tomography from sparse data"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"
    - "can fully convolutional networks perform well for general image restoration problems"

Connected through hyperedge HE-5 (induced by source paper "few labeled atlases are necessary for deep learning based segmentation"):
  with members:
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "learning a probabilistic model for diffeomorphic registration"
    - "semi supervised and task driven data augmentation"
    - "unsupervised learning of probabilistic diffeomorphic registration for images and surfaces"
    - "voxelmorph a learning framework for deformable medical image registration"
    - "a deep learning framework for unsupervised affine and deformable image registration"
    - "weakly supervised convolutional neural networks for multimodal image registration"
    - "anatomical priors in convolutional networks for unsupervised biomedical segmentation"

Connected through hyperedge HE-6 (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"
    - "auto context convolutional neural network auto net for brain extraction in magnetic resonance imaging"
    - "brain tumor segmentation with deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "brain segmentation based on multi atlas guided 3d fully convolutional network ensembles"):
  with members:
    - "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"
    - "deep neural networks for anatomical brain segmentation"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "scalable neural architecture search for 3d medical image segmentation"):
  with members:
    - "nnu net self adapting framework for u net based medical image segmentation"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "attention u net learning where to look for the pancreas"
    - "efficient neural architecture search via parameter sharing"
    - "learning transferable architectures for scalable image recognition"
    - "convolutional networks with adaptive inference graphs"
    - "learned optimizers that scale and generalize"

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)