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: "automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks"
  Abstract: A cascade of fully convolutional neural networks is proposed to segment multi-modal Magnetic Resonance (MR) images with brain tumor into background and three hierarchical regions: whole tumor, tumor core and enhancing tumor core. The cascade is designed to decompose the multi-class segmentation problem into a sequence of three binary segmentation problems according to the subregion hierarchy. The whole tumor is segmented in the first step and the bounding box of the result is used for the tumor core segmentation in the second step. The enhancing tumor core is then segmented based on the bounding box of the tumor core segmentation result. Our networks consist of multiple layers of anisotropic and dilated convolution filters, and they are combined with multi-view fusion to reduce false positives. Residual connections and multi-scale predictions are employed in these networks to boost the segmentation performance. Experiments with BraTS 2017 validation set show that the proposed method achieved average Dice scores of 0.7859, 0.9050, 0.8378 for enhancing tumor core, whole tumor and tumor core, respectively. The corresponding values for BraTS 2017 testing set were 0.7831, 0.8739, and 0.7748, respectively.

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"
    - "no new net"
    - "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"

Connected through hyperedge HE-2 (induced by source paper "task decomposition and synchronization for semantic biomedical image segmentation"):
  with members:
    - "exfuse enhancing feature fusion for semantic segmentation"
    - "anatomical priors in convolutional networks for unsupervised biomedical segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "context encoding for semantic segmentation"
    - "linknet exploiting encoder representations for efficient semantic segmentation"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "joint sequence learning and cross modality convolution for 3d biomedical segmentation"
    - "a survey on multi task learning"

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"
    - "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-4 (induced by source paper "brain tumor segmentation on mri with missing modalities"):
  with members:
    - "deep recurrent level set for segmenting brain tumors"
    - "deep depth completion of a single rgb d image"
    - "ra unet a hybrid deep attention aware network to extract liver and tumor in ct scans"
    - "brain tumor segmentation and tractographic feature extraction from structural mr images for overall survival prediction"
    - "brain tumor segmentation with deep neural networks"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "hemis hetero modal image segmentation"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-5 (induced by source paper "semi supervised variational autoencoder for survival prediction"):
  with members:
    - "no new net"
    - "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"
    - "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 "niftynet a deep learning platform for medical imaging"):
  with members:
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "freehand ultrasound image simulation with spatially conditioned generative adversarial networks"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "a self aware sampling scheme to efficiently train fully convolutional networks for semantic segmentation"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "a survey on deep learning in medical image analysis"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"

Connected through hyperedge HE-7 (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"
    - "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"
    - "brain tumor segmentation with deep neural networks"
    - "ensembles of multiple models and architectures for robust brain tumour segmentation"

Connected through hyperedge HE-8 (induced by source paper "no new net"):
  with members:
    - "brain tumor segmentation and radiomics survival prediction contribution to the brats 2017 challenge"
    - "attention u net learning where to look for the pancreas"
    - "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"
    - "cnn based segmentation of medical imaging data"
    - "ensembles of multiple models and architectures for robust brain tumour segmentation"
    - "automatic cardiac disease assessment on cine mri via time series segmentation and domain specific features"
    - "h denseunet hybrid densely connected unet for liver and liver tumor segmentation from ct volumes"

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)