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: "3d anisotropic hybrid network transferring convolutional features from 2d images to 3d anisotropic volumes"
  Abstract: While deep convolutional neural networks (CNN) have been successfully applied for 2D image analysis, it is still challenging to apply them to 3D anisotropic volumes, especially when the within-slice resolution is much higher than the between-slice resolution and when the amount of 3D volumes is relatively small. On one hand, direct learning of CNN with 3D convolution kernels suffers from the lack of data and likely ends up with poor generalization; insufficient GPU memory limits the model size or representational power. On the other hand, applying 2D CNN with generalizable features to 2D slices ignores between-slice information. Coupling 2D network with LSTM to further handle the between-slice information is not optimal due to the difficulty in LSTM learning. To overcome the above challenges, we propose a 3D Anisotropic Hybrid Network (AH-Net) that transfers convolutional features learned from 2D images to 3D anisotropic volumes. Such a transfer inherits the desired strong generalization capability for within-slice information while naturally exploiting between-slice information for more effective modelling. The focal loss is further utilized for more effective end-to-end learning. We experiment with the proposed 3D AH-Net on two different medical image analysis tasks, namely lesion detection from a Digital Breast Tomosynthesis volume, and liver and liver tumor segmentation from a Computed Tomography volume and obtain the state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "thickened 2d networks for efficient 3d medical image segmentation"):
  with members:
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "long term feature banks for detailed video understanding"
    - "compact generalized non local network"
    - "3d semi supervised learning with uncertainty aware multi view co training"
    - "differentiable learning to normalize via switchable normalization"
    - "attention u net learning where to look for the pancreas"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "bridging the gap between 2d and 3d organ segmentation with volumetric fusion net"

Connected through hyperedge HE-2 (induced by source paper "neurreg neural registration and its application to image segmentation"):
  with members:
    - "learning a probabilistic model for diffeomorphic registration"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "neural multi scale self supervised registration for echocardiogram dense tracking"
    - "voxelmorph a learning framework for deformable medical image registration"
    - "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "airnet self supervised affine registration for 3d medical images using neural networks"
    - "an unsupervised learning model for deformable medical image registration"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised segmentation from extreme points"):
  with members:
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "interactive segmentation of medical images through fully convolutional neural networks"
    - "constrained cnn losses for weakly supervised segmentation"
    - "accurate weakly supervised deep lesion segmentation on ct scans self paced 3d mask generation from recist"
    - "self learning to detect and segment cysts in lung ct images without manual annotation"
    - "accurate weakly supervised deep lesion segmentation using large scale clinical annotations slice propagated 3d mask generation from 2d recist"
    - "interactive medical image segmentation using deep learning with image specific fine tuning"
    - "learning to segment medical images with scribble supervision alone"

Connected through hyperedge HE-4 (induced by source paper "extracting 2d weak labels from volume labels using multiple instance learning in ct hemorrhage detection"):
  with members:
    - "distributed deep learning for robust multi site segmentation of ct imaging after traumatic brain injury"
    - "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"
    - "terabyte scale deep multiple instance learning for classification and localization in pathology"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "constrained deep weak supervision for histopathology image segmentation"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-5 (induced by source paper "inn inflated neural networks for ipmn diagnosis"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "deep multi modal classification of intraductal papillary mucinous neoplasms ipmn with canonical correlation analysis"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "densely connected convolutional networks"
    - "rethinking the inception architecture for computer vision"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-6 (induced by source paper "3d semi supervised learning with uncertainty aware multi view co training"):
  with members:
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "unsupervised domain adaptation for automatic estimation of cardiothoracic ratio"
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "deep co training for semi supervised image recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "rethinking atrous convolution for semantic image segmentation"

Connected through hyperedge HE-7 (induced by source paper "bottleneck supervised u net for pixel wise liver and tumor segmentation"):
  with members:
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "ivd net intervertebral disc localization and segmentation in mri with a multi modal unet"
    - "brain tumor segmentation with deep neural networks"
    - "deep photo style transfer"
    - "h denseunet hybrid densely connected unet for liver and liver tumor segmentation from ct volumes"
    - "automated unsupervised segmentation of liver lesions in ct scans via cahn hilliard phase separation"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-8 (induced by source paper "elastic boundary projection for 3d medical imaging segmentation"):
  with members:
    - "bridging the gap between 2d and 3d organ segmentation with volumetric fusion net"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "brain tumor segmentation with deep neural networks"
    - "spatial aggregation of holistically nested networks for automated pancreas segmentation"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "densely connected convolutional networks"
    - "v net fully convolutional neural networks for volumetric medical image 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)