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: "h denseunet hybrid densely connected unet for liver and liver tumor segmentation from ct volumes"
  Abstract: Liver cancer is one of the leading causes of cancer death. To assist doctors in hepatocellular carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor segmentation method is highly demanded in the clinical practice. Recently, fully convolutional neural networks (FCNs), including 2D and 3D FCNs, serve as the back-bone in many volumetric image segmentation. However, 2D convolutions can not fully leverage the spatial information along the third dimension while 3D convolutions suffer from high computational cost and GPU memory consumption. To address these issues, we propose a novel hybrid densely connected UNet (H-DenseUNet), which consists of a 2D DenseUNet for efficiently extracting intra-slice features and a 3D counterpart for hierarchically aggregating volumetric contexts under the spirit of the auto-context algorithm for liver and tumor segmentation. We propose a new training strategy by formulating the learning process of H-DenseUNet in an end-to-end manner, where the intra-slice representations and inter-slice features can be jointly optimized through a hybrid feature fusion (HFF) layer. We extensively evaluated our method on the dataset of MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge. Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.

Connected through hyperedge HE-1 (induced by source paper "convolutional networks with dense connectivity"):
  with members:
    - "log densenet how to sparsify a densenet"
    - "squeeze and excitation networks"
    - "dual path networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "condensenet an efficient densenet using learned group convolutions"
    - "resnet in resnet generalizing residual architectures"
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-2 (induced by source paper "deep sequential segmentation of organs in volumetric medical scans"):
  with members:
    - "densely dilated spatial pooling convolutional network using benign loss functions for imbalanced volumetric prostate segmentation"
    - "lstm a search space odyssey"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "an exploration of 2d and 3d deep learning techniques for cardiac mr image segmentation"
    - "extracting 3d vascular structures from microscopy images using convolutional recurrent networks"
    - "automatic brain tumor detection and segmentation using u net based fully convolutional networks"
    - "3 d convolutional neural networks for glioblastoma segmentation"

Connected through hyperedge HE-3 (induced by source paper "identify the cells nuclei based on the deep learning neural network"):
  with members:
    - "what makes for effective detection proposals"
    - "feature pyramid networks for object detection"
    - "deep residual learning for image recognition"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "deep learning in radiology an overview of the concepts and a survey of the state of the art"):
  with members:
    - "segmentation of the proximal femur from mr images using deep convolutional neural networks"
    - "real time automatic fetal brain extraction in fetal mri by deep learning"
    - "automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "brain tumor segmentation with deep neural networks"
    - "an analysis of deep neural network models for practical applications"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "focal loss for dense object detection"

Connected through hyperedge HE-5 (induced by source paper "msnet mutual suppression network for disentangled video representations"):
  with members:
    - "decomposing motion and content for natural video sequence prediction"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "folded recurrent neural networks for future video prediction"
    - "unsupervised learning of disentangled representations from video"
    - "deep learning for precipitation nowcasting a benchmark and a new model"
    - "spatio temporal action detection with cascade proposal and location anticipation"
    - "an end to end 3d convolutional neural network for action detection and segmentation in videos"
    - "mocogan decomposing motion and content for video generation"

Connected through hyperedge HE-6 (induced by source paper "3d roi aware u net for accurate and efficient colorectal tumor segmentation"):
  with members:
    - "estimation of the volume of the left ventricle from mri images using deep neural networks"
    - "path aggregation network for instance segmentation"
    - "crossbar net a novel convolutional network for kidney tumor segmentation in ct images"
    - "segmentation by detection a cascade network for volumetric medical image segmentation"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks"
    - "mask r cnn"
    - "instance normalization the missing ingredient for fast stylization"

Connected through hyperedge HE-7 (induced by source paper "hyperdense net a hyper densely connected cnn for multi modal image segmentation"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "scalable multimodal convolutional networks for brain tumour segmentation"
    - "multi scale dense networks for resource efficient image classification"
    - "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach"
    - "isointense infant brain segmentation with a hyper dense connected convolutional neural network"
    - "deep cnn ensembles and suggestive annotations for infant brain mri segmentation"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion 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"
    - "automatic brain tumor segmentation using cascaded anisotropic convolutional neural 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"

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)