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: "abdominal multi organ segmentation with organ attention networks and statistical fusion"
  Abstract: Abstract   Accurate and robust segmentation of abdominal organs on CT is essential for many clinical applications such as computer-aided diagnosis and computer-aided surgery. But this task is challenging due to the weak boundaries of organs, the complexity of the background, and the variable sizes of different organs. To address these challenges, we introduce a novel framework for multi-organ segmentation of abdominal regions by using organ-attention networks with reverse connections (OAN-RCs) which are applied to 2D views, of the 3D CT volume, and output estimates which are combined by statistical fusion exploiting structural similarity. More specifically, OAN is a two-stage deep convolutional network, where deep network features from the first stage are combined with the original image, in a second stage, to reduce the complex background and enhance the discriminative information for the target organs. Intuitively, OAN reduces the effect of the complex background by focusing attention so that each organ only needs to be discriminated from its local background. RCs are added to the first stage to give the lower layers more semantic information thereby enabling them to adapt to the sizes of different organs. Our networks are trained on 2D views (slices) enabling us to use holistic information and allowing efficient computation (compared to using 3D patches). To compensate for the limited cross-sectional information of the original 3D volumetric CT, e.g., the connectivity between neighbor slices, multi-sectional images are reconstructed from the three different 2D view directions. Then we combine the segmentation results from the different views using statistical fusion, with a novel term relating the structural similarity of the 2D views to the original 3D structure. To train the network and evaluate results, 13 structures were manually annotated by four human raters and confirmed by a senior expert on 236 normal cases. We tested our algorithm by 4-fold cross-validation and computed Dice–Sorensen similarity coefficients (DSC) and surface distances for evaluating our estimates of the 13 structures. Our experiments show that the proposed approach gives strong results and outperforms 2D- and 3D-patch based state-of-the-art methods in terms of DSC and mean surface distances.

Connected through hyperedge HE-1 (induced by source paper "multi organ segmentation over partially labeled datasets with multi scale feature abstraction"):
  with members:
    - "boundary weighted domain adaptive neural network for prostate mr image segmentation"
    - "learning a deep convnet for multi label classification with partial labels"
    - "use net incorporating squeeze and excitation blocks into u net for prostate zonal segmentation of multi institutional mri datasets"
    - "feature fusion encoder decoder network for automatic liver lesion segmentation"
    - "ce net context encoder network for 2d medical image segmentation"
    - "the liver tumor segmentation benchmark lits"
    - "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation"
    - "joint optic disc and cup segmentation based on multi label deep network and polar transformation"

Connected through hyperedge HE-2 (induced by source paper "abdominal multi organ segmentation with cascaded convolutional and adversarial deep networks"):
  with members:
    - "breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network"
    - "a multi scale pyramid of 3d fully convolutional networks for abdominal multi organ segmentation"
    - "unet a nested u net architecture for medical image segmentation"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "auto context convolutional neural network auto net for brain extraction in magnetic resonance imaging"
    - "hierarchical 3d fully convolutional networks for multi organ segmentation"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-3 (induced by source paper "understanding adversarial attacks on deep learning based medical image analysis systems"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "skip connections matter on the transferability of adversarial examples generated with resnets"
    - "one pixel attack for fooling deep neural networks"
    - "interpreting and improving adversarial robustness with neuron sensitivity"
    - "towards noise robust neural networks via progressive adversarial training"
    - "black box adversarial attacks on video recognition models"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "security analysis and enhancement of model compressed deep learning systems under adversarial attacks"

Connected through hyperedge HE-4 (induced by source paper "multi scale guided attention for medical image segmentation"):
  with members:
    - "deep q learning driven ct pancreas segmentation with geometry aware u net"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "multiregion segmentation of bladder cancer structures in mri with progressive dilated convolutional networks"
    - "an attention model for group level emotion recognition"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "attention gated networks learning to leverage salient regions in medical images"
    - "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"
    - "mask r cnn with pyramid attention network for scene text detection"

Connected through hyperedge HE-5 (induced by source paper "prior aware neural network for partially supervised multi organ segmentation"):
  with members:
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "constrained cnn losses for weakly supervised segmentation"
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "a multi scale pyramid of 3d fully convolutional networks for abdominal multi organ segmentation"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "anatomical priors in convolutional networks for unsupervised biomedical segmentation"
    - "on regularized losses for weakly supervised cnn segmentation"

Connected through hyperedge HE-6 (induced by source paper "deep distance transform for tubular structure segmentation in ct scans"):
  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"
    - "nnu net breaking the spell on successful medical image segmentation"
    - "kid net convolution networks for kidney vessels segmentation from ct volumes"
    - "deepflux for skeletons in the wild"
    - "3d semi supervised learning with uncertainty aware multi view co training"
    - "training multi organ segmentation networks with sample selection by relaxed upper confident bound"
    - "hi fi hierarchical feature integration for skeleton detection"

Connected through hyperedge HE-7 (induced by source paper "local block wise self attention for normal organ segmentation"):
  with members:
    - "image transformer"
    - "ocnet object context network for scene parsing"
    - "ccnet criss cross attention for semantic segmentation"
    - "attention u net learning where to look for the pancreas"
    - "dual attention network for scene segmentation"
    - "non local neural networks"
    - "attention is all you need"
    - "relation networks for object detection"

Connected through hyperedge HE-8 (induced by source paper "semi supervised multi organ segmentation via deep multi planar co training"):
  with members:
    - "a multi scale pyramid of 3d fully convolutional networks for abdominal multi organ segmentation"
    - "deep co training for semi supervised image recognition"
    - "pcl proposal cluster learning for weakly supervised object detection"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "data distillation towards omni supervised learning"
    - "progressive and multi path holistically nested neural networks for pathological lung segmentation from ct images"
    - "hierarchical 3d fully convolutional networks for multi organ 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)