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: "hierarchical 3d fully convolutional networks for multi organ segmentation"
  Abstract: Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of full volumetric images. In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of seven abdominal structures (artery, vein, liver, spleen, stomach, gallbladder, and pancreas) can achieve competitive segmentation results, while avoiding the need for handcrafting features or training organ-specific models. To this end, we propose a two-stage, coarse-to-fine approach that trains an FCN model to roughly delineate the organs of interest in the first stage (seeing $\sim$40% of the voxels within a simple, automatically generated binary mask of the patient's body). We then use these predictions of the first-stage FCN to define a candidate region that will be used to train a second FCN. This step reduces the number of voxels the FCN has to classify to $\sim$10% while maintaining a recall high of $>$99%. This second-stage FCN can now focus on more detailed segmentation of the organs. We respectively utilize training and validation sets consisting of 281 and 50 clinical CT images. Our hierarchical approach provides an improved Dice score of 7.5 percentage points per organ on average in our validation set. We furthermore test our models on a completely unseen data collection acquired at a different hospital that includes 150 CT scans with three anatomical labels (liver, spleen, and pancreas). In such challenging organs as the pancreas, our hierarchical approach improves the mean Dice score from 68.5 to 82.2%, achieving the highest reported average score on this dataset.

Connected through hyperedge HE-1 (induced by source paper "abdominal multi organ segmentation with cascaded convolutional and adversarial deep networks"):
  with members:
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "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"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-2 (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"
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "abdominal multi organ segmentation with organ attention networks and statistical fusion"):
  with members:
    - "towards dense volumetric pancreas segmentation in ct using 3d fully convolutional networks"
    - "an application of cascaded 3d fully convolutional networks for medical image segmentation"
    - "brain tumor segmentation with deep neural networks"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "ron reverse connection with objectness prior networks for object detection"
    - "spatial aggregation of holistically nested networks for automated pancreas segmentation"
    - "deepcut object segmentation from bounding box annotations using convolutional neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-4 (induced by source paper "attention u net learning where to look for the pancreas"):
  with members:
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "ternarynet faster deep model inference without gpus for medical 3d segmentation using sparse and binary convolutions"
    - "fully convolutional multi scale residual densenets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers"
    - "learn to pay attention"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "non local neural networks"
    - "automated cardiovascular magnetic resonance image analysis with fully convolutional networks"
    - "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function"

Connected through hyperedge HE-5 (induced by source paper "semi supervised multi organ segmentation via deep multi planar co training"):
  with members:
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "deep learning and its application to medical image segmentation"):
  with members:
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "towards dense volumetric pancreas segmentation in ct using 3d fully convolutional networks"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "spatial aggregation of holistically nested convolutional neural networks for automated pancreas localization and segmentation"
    - "spatial aggregation of holistically nested networks for automated pancreas segmentation"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "the importance of skip connections in biomedical image segmentation"
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"

Connected through hyperedge HE-7 (induced by source paper "computational anatomy for multi organ analysis in medical imaging a review"):
  with members:
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "a generic approach to lung field segmentation from chest radiographs using deep space and shape learning"
    - "a multi scale pyramid of 3d fully convolutional networks for abdominal multi organ segmentation"
    - "deep learning and its application to medical image segmentation"
    - "tracking the trackers an analysis of the state of the art in multiple object tracking"
    - "towards automatic abdominal multi organ segmentation in dual energy ct using cascaded 3d fully convolutional network"
    - "hough cnn deep learning for segmentation of deep brain regions in mri and ultrasound"
    - "automatic segmentation of mr brain images with a convolutional neural network"

Connected through hyperedge HE-8 (induced by source paper "training multi organ segmentation networks with sample selection by relaxed upper confident bound"):
  with members:
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "multi stage multi recursive input fully convolutional networks for neuronal boundary detection"
    - "spatial aggregation of holistically nested networks for automated pancreas segmentation"
    - "training region based object detectors with online hard example mining"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "a fixed point model for pancreas segmentation in abdominal ct scans"
    - "dense volume to volume vascular boundary detection"
    - "u net convolutional networks for biomedical 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)