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: "an application of cascaded 3d fully convolutional networks for medical image segmentation"
  Abstract: Abstract   Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of volumetric images. In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of several anatomical structures (ranging from the large organs to thin vessels) can achieve competitive segmentation results, while avoiding the need for handcrafting features or training class-specific models.  To this end, we propose a two-stage, coarse-to-fine approach that will first use a 3D FCN to roughly define a candidate region, which will then be used as input to a second 3D FCN. This reduces the number of voxels the second FCN has to classify to ∼10% and allows it to focus on more detailed segmentation of the organs and vessels.  We utilize training and validation sets consisting of 331 clinical CT images and test our models on a completely unseen data collection acquired at a different hospital that includes 150 CT scans, targeting three anatomical organs (liver, spleen, and pancreas). In challenging organs such as the pancreas, our cascaded approach improves the mean Dice score from 68.5 to 82.2%, achieving the highest reported average score on this dataset. We compare with a 2D FCN method on a separate dataset of 240 CT scans with 18 classes and achieve a significantly higher performance in small organs and vessels. Furthermore, we explore fine-tuning our models to different datasets.  Our experiments illustrate the promise and robustness of current 3D FCN based semantic segmentation of medical images, achieving state-of-the-art results. 1

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"
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "mds net a model driven stack based fully convolutional network for pancreas segmentation"):
  with members:
    - "deep q learning driven ct pancreas segmentation with geometry aware u net"
    - "pancreas segmentation in ct and mri images via domain specific network designing and recurrent neural contextual learning"
    - "combining data driven and model driven methods for robust facial landmark detection"
    - "unet a nested u net architecture for medical image segmentation"
    - "towards dense volumetric pancreas segmentation in ct using 3d fully convolutional networks"
    - "model driven deep learning for physical layer communications"
    - "joint cs mri reconstruction and segmentation with a unified deep network"
    - "lstm a search space odyssey"

Connected through hyperedge HE-3 (induced by source paper "probability map guided bi directional recurrent unet for pancreas segmentation"):
  with members:
    - "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"
    - "pancreas segmentation in ct and mri images via domain specific network designing and recurrent neural contextual learning"
    - "ra unet a hybrid deep attention aware network to extract liver and tumor in ct scans"
    - "3d semi supervised learning with uncertainty aware multi view co training"
    - "attention u net learning where to look for the pancreas"
    - "automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "a 3d coarse to fine framework for volumetric medical image segmentation"

Connected through hyperedge HE-4 (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"
    - "brain tumor segmentation with deep neural networks"
    - "hierarchical 3d fully convolutional networks for multi organ segmentation"
    - "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-5 (induced by source paper "globally guided progressive fusion network for 3d pancreas segmentation"):
  with members:
    - "bridging the gap between 2d and 3d organ segmentation with volumetric fusion net"
    - "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-6 (induced by source paper "fully convolutional networks and generative adversarial networks applied to sclera segmentation"):
  with members:
    - "a benchmark for iris location and a deep learning detector evaluation"
    - "segdensenet iris segmentation for pre and post cataract surgery"
    - "a robust real time automatic license plate recognition based on the yolo detector"
    - "road segmentation in sar satellite images with deep fully convolutional neural networks"
    - "semantic segmentation using adversarial networks"
    - "spatially supervised recurrent convolutional neural networks for visual object tracking"
    - "image to image translation with conditional adversarial networks"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-7 (induced by source paper "robust iris segmentation based on fully convolutional networks and generative adversarial networks"):
  with members:
    - "a benchmark for iris location and a deep learning detector evaluation"
    - "fully convolutional networks and generative adversarial networks applied to sclera segmentation"
    - "a robust real time automatic license plate recognition based on the yolo detector"
    - "road segmentation in sar satellite images with deep fully convolutional neural networks"
    - "multinet real time joint semantic reasoning for autonomous driving"
    - "semantic segmentation using adversarial networks"
    - "image to image translation with conditional adversarial networks"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-8 (induced by source paper "imperfect segmentation labels how much do they matter"):
  with members:
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "tensorflow a system for large scale machine learning"
    - "u net convolutional networks for biomedical image segmentation"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "deeporgan multi level deep convolutional networks for automated pancreas segmentation"
    - "adam a method for stochastic optimization"
    - "training deep neural networks on noisy labels with bootstrapping"
    - "fully convolutional networks for semantic 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)