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: "towards automatic 3d shape instantiation for deployed stent grafts 2d multiple class and class imbalance marker segmentation with equally weighted focal u net"
  Abstract: Robot-assisted Fenestrated Endovascular Aortic Repair (FEVAR) is currently navigated by 2D fluoroscopy which is insufficiently informative. Previously, a semi-automatic 3D shape instantiation method was developed to instantiate the 3D shape of a main, deployed, and fenestrated stent graft from a single fluoroscopy projection in real-time, which could help 3D FEVAR navigation and robotic path planning. This proposed semi-automatic method was based on the Robust Perspective-5-Point (RP5P) method, graft gap interpolation and semi-automatic multiple-class marker center determination. In this paper, an automatic 3D shape instantiation could be achieved by automatic multiple-class marker segmentation and hence automatic multiple-class marker center determination. Firstly, the markers were designed into five different shapes. Then, Equally-weighted Focal U-Net was proposed to segment the fluoroscopy projections of customized markers into five classes and hence to determine the marker centers. The proposed Equally-weighted Focal U-Net utilized U-Net as the network architecture, equally-weighted loss function for initial marker segmentation, and then equally-weighted focal loss function for improving the initial marker segmentation. This proposed network outperformed traditional Weighted U-Net on the class-imbalance segmentation in this paper with reducing one hyper-parameter - the weight. An overall mean Intersection over Union (mIoU) of 0.6943 was achieved on 78 testing images, where 81.01% markers were segmented with a center position error <1.6mm. Comparable accuracy of 3D shape instantiation was also achieved and stated. The data, trained models and TensorFlow codes are available on-line.

Connected through hyperedge HE-1 (induced by source paper "acnn a full resolution dcnn for medical image segmentation"):
  with members:
    - "real time 3d shape instantiation for partially deployed stent segment from a single 2d fluoroscopic image in robot assisted fenestrated endovascular aortic repair"
    - "group normalization"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "focal loss for dense object detection"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "dual path networks"
    - "understanding convolution for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "real time 3d shape instantiation for partially deployed stent segment from a single 2d fluoroscopic image in robot assisted fenestrated endovascular aortic repair"):
  with members:
    - "3d path planning from a single 2d fluoroscopic image for robot assisted fenestrated endovascular aortic repair"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "spectral networks and locally connected networks on graphs"

Connected through hyperedge HE-3 (induced by source paper "normalization in training u net for 2d biomedical semantic segmentation"):
  with members:
    - "group normalization"
    - "brain tumor segmentation and tractographic feature extraction from structural mr images for overall survival prediction"
    - "fully automatic detection and segmentation of abdominal aortic thrombus in post operative cta images using deep convolutional neural networks"
    - "batch instance normalization for adaptively style invariant neural networks"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "don t decay the learning rate increase the batch size"
    - "a survey on deep learning in medical image analysis"
    - "cosine normalization using cosine similarity instead of dot product in neural networks"

Connected through hyperedge HE-4 (induced by source paper "multiclass weighted loss for instance segmentation of cluttered cells"):
  with members:
    - "gland instance segmentation using deep multichannel neural networks"
    - "mask r cnn"
    - "what makes for effective detection proposals"
    - "gland segmentation in colon histology images the glas challenge contest"
    - "dcan deep contour aware networks for accurate gland segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "3d path planning from a single 2d fluoroscopic image for robot assisted fenestrated endovascular aortic repair"):
  with members:
    - "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)