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: "bridging the gap between 2d and 3d organ segmentation with volumetric fusion net"
  Abstract: There has been a debate on whether to use 2D or 3D deep neural networks for volumetric organ segmentation. Both 2D and 3D models have their advantages and disadvantages. In this paper, we present an alternative framework, which trains 2D networks on different viewpoints for segmentation, and builds a 3D Volumetric Fusion Net (VFN) to fuse the 2D segmentation results. VFN is relatively shallow and contains much fewer parameters than most 3D networks, making our framework more efficient at integrating 3D information for segmentation. We train and test the segmentation and fusion modules individually, and propose a novel strategy, named cross-cross-augmentation, to make full use of the limited training data. We evaluate our framework on several challenging abdominal organs, and verify its superiority in segmentation accuracy and stability over existing 2D and 3D approaches.

Connected through hyperedge HE-1 (induced by source paper "c2fnas coarse to fine neural architecture search for 3d medical image segmentation"):
  with members:
    - "one network to segment them all a general lightweight system for accurate 3d medical image segmentation"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "single path one shot neural architecture search with uniform sampling"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "fairnas rethinking evaluation fairness of weight sharing neural architecture search"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "scalable neural architecture search for 3d medical image segmentation"

Connected through hyperedge HE-2 (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"
    - "ct realistic lung nodule simulation from 3d conditional generative adversarial networks for robust lung segmentation"

Connected through hyperedge HE-3 (induced by source paper "fusionnet incorporating shape and texture for abnormality detection in 3d abdominal ct scans"):
  with members:
    - "3d semi supervised learning with uncertainty aware multi view co training"
    - "joint shape representation and classification for detecting pdac"
    - "multi scale coarse to fine segmentation for screening pancreatic ductal adenocarcinoma"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "a 3d coarse to fine framework for volumetric medical image segmentation"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "personalized pancreatic tumor growth prediction via group learning"
    - "marrnet 3d shape reconstruction via 2 5d sketches"

Connected through hyperedge HE-4 (induced by source paper "globally guided progressive fusion network for 3d pancreas segmentation"):
  with members:
    - "an application of cascaded 3d fully convolutional networks for medical image segmentation"
    - "deep learning and its application to medical image segmentation"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "a fixed point model for pancreas segmentation in abdominal ct scans"
    - "real time video super resolution with spatio temporal networks and motion compensation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "fully automated pancreas segmentation with two stage 3d convolutional neural networks"):
  with members:
    - "probability map guided bi directional recurrent unet for pancreas segmentation"
    - "attention u net learning where to look for the pancreas"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "a 3d coarse to fine framework for volumetric medical image segmentation"
    - "spatial aggregation of holistically nested convolutional neural networks for automated pancreas localization and segmentation"
    - "spatial aggregation of holistically nested networks for automated pancreas segmentation"
    - "a fixed point model for pancreas segmentation in abdominal ct scans"
    - "deeporgan multi level deep convolutional networks for automated pancreas segmentation"

Connected through hyperedge HE-6 (induced by source paper "neural style transfer improves 3d cardiovascular mr image segmentation on inconsistent data"):
  with members:
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "unsupervised domain adaptation for automatic estimation of cardiothoracic ratio"
    - "a new ensemble learning framework for 3d biomedical image segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "dilated convolutional neural networks for cardiovascular mr segmentation in congenital heart disease"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-7 (induced by source paper "iterative reorganization with weak spatial constraints solving arbitrary jigsaw puzzles for unsupervised representation learning"):
  with members:
    - "improving spatiotemporal self supervision by deep reinforcement learning"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "deep co training for semi supervised image recognition"
    - "unsupervised representation learning by predicting image rotations"
    - "deeppermnet visual permutation learning"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "improvements to context based self supervised learning"

Connected through hyperedge HE-8 (induced by source paper "a new ensemble learning framework for 3d biomedical image segmentation"):
  with members:
    - "ensembling neural networks for digital pathology images classification and segmentation"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "multi stage multi recursive input fully convolutional networks for neuronal boundary detection"
    - "dilated convolutional neural networks for cardiovascular mr segmentation in congenital heart disease"
    - "xgboost a scalable tree boosting system"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "densely connected convolutional networks"

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)