human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (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"):
  Abstract: In robot-assisted Fenestrated Endovascular Aortic Repair (FEVAR), accurate alignment of stent graft fenestrations or scallops with aortic branches is essential for establishing complete blood flow perfusion. Current navigation is largely based on 2D fluoroscopic images, which lacks 3D anatomical information, thus causing longer operation time as well as high risks of radiation exposure. Previously, 3D shape instantiation frameworks for real-time 3D shape reconstruction of fully-deployed or fully-compressed stent graft from a single 2D fluoroscopic image have been proposed for 3D navigation in robot-assisted FEVAR. However, these methods could not instantiate partially-deployed stent segments, as the 3D marker references are unknown. In this paper, an adapted Graph Convolutional Network (GCN) is proposed to predict 3D marker references from 3D fully-deployed markers. As original GCN is for classification, in this paper, the coarsening layers are removed and the softmax function at the network end is replaced with linear mapping for the regression task. The derived 3D and the 2D marker references are used to instantiate partially-deployed stent segment shape with the existing 3D shape instantiation framework. Validations were performed on three commonly used stent grafts and five patient-specific 3D printed aortic aneurysm phantoms. Comparable performances with average mesh distance errors of 1$\sim$3mm and average angular errors around 7degree were achieved.

Includes members:
  Member "3d path planning from a single 2d fluoroscopic image for robot assisted fenestrated endovascular aortic repair":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "one stage shape instantiation from a single 2d image to 3d point cloud")
  Member "towards automatic 3d shape instantiation for deployed stent grafts 2d multiple class and class imbalance marker segmentation with equally weighted focal u net":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "acnn a full resolution dcnn for medical image segmentation")
  Member "semi supervised classification with graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "convolutional neural networks on graphs with fast localized spectral filtering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "spectral networks and locally connected networks on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")

Question: Please tell me which class the source paper 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)