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: "a deep learning model integrating fcnns and crfs for brain tumor segmentation"
  Abstract: Accurate and reliable brain tumor segmentation is a critical component in cancer diagnosis, treatment planning, and treatment outcome evaluation. Build upon successful deep learning techniques, a novel brain tumor segmentation method is developed by integrating fully convolutional neural networks (FCNNs) and Conditional Random Fields (CRFs) in a unified framework to obtain segmentation results with appearance and spatial consistency. We train a deep learning based segmentation model using 2D image patches and image slices in following steps: 1) training FCNNs using image patches; 2) training CRFs as Recurrent Neural Networks (CRF-RNN) using image slices with parameters of FCNNs fixed; and 3) fine-tuning the FCNNs and the CRF-RNN using image slices. Particularly, we train 3 segmentation models using 2D image patches and slices obtained in axial, coronal and sagittal views respectively, and combine them to segment brain tumors using a voting based fusion strategy. Our method could segment brain images slice-by-slice, much faster than those based on image patches. We have evaluated our method based on imaging data provided by the Multimodal Brain Tumor Image Segmentation Challenge (BRATS) 2013, BRATS 2015 and BRATS 2016. The experimental results have demonstrated that our method could build a segmentation model with Flair, T1c, and T2 scans and achieve competitive performance as those built with Flair, T1, T1c, and T2 scans.

Connected through hyperedge HE-1 (induced by source paper "robust multimodal brain tumor segmentation via feature disentanglement and gated fusion"):
  with members:
    - "unsupervised deformable registration for multi modal images via disentangled representations"
    - "brain tumor segmentation on mri with missing modalities"
    - "factorised spatial representation learning application in semi supervised myocardial segmentation"
    - "multimodal unsupervised image to image translation"
    - "learning factorized multimodal representations"
    - "improving cnn training using disentanglement for liver lesion classification in ct"
    - "scalable multimodal convolutional networks for brain tumour segmentation"
    - "joint sequence learning and cross modality convolution for 3d biomedical segmentation"

Connected through hyperedge HE-2 (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-3 (induced by source paper "a feature transfer enabled multi task deep learning model on medical imaging"):
  with members:
    - "an interpretable deep hierarchical semantic convolutional neural network for lung nodule malignancy classification"
    - "sd cnn a shallow deep cnn for improved breast cancer diagnosis"
    - "brain tumor segmentation with deep neural networks"
    - "a survey on deep learning in medical image analysis"
    - "an overview of multi task learning in deep neural networks"
    - "mask r cnn"
    - "the importance of skip connections in biomedical image segmentation"
    - "improving computer aided detection using convolutional neural networks and random view aggregation"

Connected through hyperedge HE-4 (induced by source paper "semi supervised brain lesion segmentation with an adapted mean teacher model"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "semi supervised deep learning for fully convolutional networks"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "temporal ensembling for semi supervised learning"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "unsupervised domain adaptation in brain lesion segmentation with adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "fully automatic segmentation of kidneys in clinical ultrasound images using a boundary distance regression network"):
  with members:
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "u net convolutional networks for biomedical image segmentation"
    - "semi automatic segmentation of autosomal dominant polycystic kidneys using random forests"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "a volumetric convolutional neural network for brain tumor segmentation"):
  with members:
    - "adaptive feature recombination and recalibration for semantic segmentation application to brain tumor segmentation in mri"
    - "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"
    - "reverse classification accuracy predicting segmentation performance in the absence of ground truth"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "generalised wasserstein dice score for imbalanced multi class segmentation using holistic convolutional networks"
    - "multimodal mri brain tumor segmentation using random forests with features learned from fully convolutional neural network"

Connected through hyperedge HE-7 (induced by source paper "convolutional herbal prescription building method from multi scale facial features"):
  with members:
    - "towards alzheimer s disease classification through transfer learning"
    - "squeeze and excitation networks"
    - "a survey on deep learning in medical image analysis"
    - "aggregated residual transformations for deep neural networks"
    - "densely connected convolutional networks"
    - "wide residual networks"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-8 (induced by source paper "adaptive feature recombination and recalibration for semantic segmentation application to brain tumor segmentation in mri"):
  with members:
    - "ensembles of multiple models and architectures for robust brain tumour segmentation"
    - "squeeze and excitation networks"
    - "aggregated residual transformations for deep neural networks"
    - "identity mappings in deep residual networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "multi scale context aggregation by dilated convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "network in network"

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)