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: "a multi scale pyramid of 3d fully convolutional networks for abdominal multi organ segmentation"
  Abstract: Recent advances in deep learning, like 3D fully convolutional networks (FCNs), have improved the state-of-the-art in dense semantic segmentation of medical images. However, most network architectures require severely downsampling or cropping the images to meet the memory limitations of today's GPU cards while still considering enough context in the images for accurate segmentation. In this work, we propose a novel approach that utilizes auto-context to perform semantic segmentation at higher resolutions in a multi-scale pyramid of stacked 3D FCNs. We train and validate our models on a dataset of manually annotated abdominal organs and vessels from 377 clinical CT images used in gastric surgery, and achieve promising results with close to 90% Dice score on average. For additional evaluation, we perform separate testing on datasets from different sources and achieve competitive results, illustrating the robustness of the model and approach.

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"
    - "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-2 (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"
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "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-3 (induced by source paper "interactive segmentation of medical images through fully convolutional neural networks"):
  with members:
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "interactive full image segmentation by considering all regions jointly"
    - "nnu net self adapting framework for u net based medical image segmentation"
    - "deep extreme cut from extreme points to object segmentation"
    - "dltk state of the art reference implementations for deep learning on medical images"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-4 (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"
    - "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"

Connected through hyperedge HE-5 (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"
    - "deep learning and its application to medical image segmentation"
    - "tracking the trackers an analysis of the state of the art in multiple object tracking"
    - "hierarchical 3d fully convolutional networks for multi organ segmentation"
    - "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"

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)