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: "aorta segmentation for stent simulation"
  Abstract: Simulation of arterial stenting procedures prior to intervention allows for appropriate device selection as well as highlights potential complications. To this end, we present a framework for facilitating virtual aortic stenting from a contrast computer tomography (CT) scan. More specifically, we present a method for both lumen and outer wall segmentation that may be employed in determining both the appropriateness of intervention as well as the selection and localization of the device. The more challenging recovery of the outer wall is based on a novel minimal closure tracking algorithm. Our aortic segmentation method has been validated on over 3000 multiplanar reformatting (MPR) planes from 50 CT angiography data sets yielding a Dice Similarity Coefficient (DSC) of 90.67%.

Connected through hyperedge HE-1 (induced by source paper "3d convolutional neural network for abdominal aortic aneurysm segmentation"):
  with members:
    - "rethinking imagenet pre training"
    - "fully automatic detection and segmentation of abdominal aortic thrombus in post operative cta images using deep convolutional neural networks"
    - "abdominal aortic aneurysm segmentation with a small number of training subjects"
    - "holistically nested edge detection"

Connected through hyperedge HE-2 (induced by source paper "fully automatic detection and segmentation of abdominal aortic thrombus in post operative cta images using deep convolutional neural networks"):
  with members:
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "hough cnn deep learning for segmentation of deep brain regions in mri and ultrasound"
    - "holistically nested edge detection"
    - "you only look once unified real time object detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "going deeper with convolutions"
    - "caffe convolutional architecture for fast feature embedding"
    - "a new 2 5d representation for lymph node detection using random sets of deep convolutional neural network observations"

Connected through hyperedge HE-3 (induced by source paper "in depth assessment of an interactive graph based approach for the segmentation for pancreatic metastasis in ultrasound acquisitions of the liver with two specialists in internal medicine"):
  with members:
    - "htc vive mevislab integration via openvr for medical applications"
    - "interactive outlining of pancreatic cancer liver metastases in ultrasound images"
    - "us cut interactive algorithm for rapid detection and segmentation of liver tumors in ultrasound acquisitions"
    - "interactive volumetry of liver ablation zones"
    - "refinement cut user guided segmentation algorithm for translational science"
    - "cube cut vertebral body segmentation in mri data through cubic shaped divergences"
    - "gbm volumetry using the 3d slicer medical image computing platform"
    - "integration of the openigtlink network protocol for image guided therapy with the medical platform mevislab"

Connected through hyperedge HE-4 (induced by source paper "interactive outlining of pancreatic cancer liver metastases in ultrasound images"):
  with members:
    - "us cut interactive algorithm for rapid detection and segmentation of liver tumors in ultrasound acquisitions"
    - "interactive volumetry of liver ablation zones"
    - "refinement cut user guided segmentation algorithm for translational science"
    - "cube cut vertebral body segmentation in mri data through cubic shaped divergences"
    - "gbm volumetry using the 3d slicer medical image computing platform"
    - "integration of the openigtlink network protocol for image guided therapy with the medical platform mevislab"
    - "pcg cut graph driven segmentation of the prostate central gland"
    - "template cut a pattern based segmentation paradigm"

Connected through hyperedge HE-5 (induced by source paper "interactive and scale invariant segmentation of the rectum sigmoid via user defined templates"):
  with members:
    - "interactive volumetry of liver ablation zones"
    - "refinement cut user guided segmentation algorithm for translational science"
    - "development of an open source software module for enhanced visualization during mr guided interstitial gynecologic brachytherapy"
    - "cube cut vertebral body segmentation in mri data through cubic shaped divergences"
    - "gbm volumetry using the 3d slicer medical image computing platform"
    - "integration of the openigtlink network protocol for image guided therapy with the medical platform mevislab"
    - "pcg cut graph driven segmentation of the prostate central gland"
    - "image guided therapy system for interstitial gynecologic brachytherapy in a multimodality operating suite"

Connected through hyperedge HE-6 (induced by source paper "cellular automata segmentation of the boundary between the compacta of vertebral bodies and surrounding structures"):
  with members:
    - "interactive volumetry of liver ablation zones"
    - "refinement cut user guided segmentation algorithm for translational science"
    - "development of an open source software module for enhanced visualization during mr guided interstitial gynecologic brachytherapy"
    - "cube cut vertebral body segmentation in mri data through cubic shaped divergences"
    - "gbm volumetry using the 3d slicer medical image computing platform"
    - "integration of the openigtlink network protocol for image guided therapy with the medical platform mevislab"
    - "image guided therapy system for interstitial gynecologic brachytherapy in a multimodality operating suite"
    - "template cut a pattern based segmentation paradigm"

Connected through hyperedge HE-7 (induced by source paper "us cut interactive algorithm for rapid detection and segmentation of liver tumors in ultrasound acquisitions"):
  with members:
    - "interactive volumetry of liver ablation zones"
    - "refinement cut user guided segmentation algorithm for translational science"
    - "cube cut vertebral body segmentation in mri data through cubic shaped divergences"
    - "integration of the openigtlink network protocol for image guided therapy with the medical platform mevislab"
    - "template cut a pattern based segmentation paradigm"
    - "square cut a segmentation algorithm on the basis of a rectangle shape"
    - "a flexible semi automatic approach for glioblastoma multiforme segmentation"
    - "pituitary adenoma segmentation"

Connected through hyperedge HE-8 (induced by source paper "interactive volumetry of liver ablation zones"):
  with members:
    - "gbm volumetry using the 3d slicer medical image computing platform"
    - "pcg cut graph driven segmentation of the prostate central gland"
    - "pituitary adenoma volumetry with 3d slicer"
    - "detection and visualization of endoleaks in ct data for monitoring of thoracic and abdominal aortic aneurysm stents"

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)