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 "cellular automata segmentation of the boundary between the compacta of vertebral bodies and surrounding structures"):
  Abstract: Due to the aging population, spinal diseases get more and more common nowadays; e.g., lifetime risk of osteoporotic fracture is 40% for white women and 13% for white men in the United States. Thus the numbers of surgical spinal procedures are also increasing with the aging population and precise diagnosis plays a vital role in reducing complication and recurrence of symptoms. Spinal imaging of vertebral column is a tedious process subjected to interpretation errors. In this contribution, we aim to reduce time and error for vertebral interpretation by applying and studying the GrowCut-algorithm for boundary segmentation between vertebral body compacta and surrounding structures. GrowCut is a competitive region growing algorithm using cellular automata. For our study, vertebral T2-weighted Magnetic Resonance Imaging (MRI) scans were first manually outlined by neurosurgeons. Then, the vertebral bodies were segmented in the medical images by a GrowCut-trained physician using the semi-automated GrowCut-algorithm. Afterwards, results of both segmentation processes were compared using the Dice Similarity Coefficient (DSC) and the Hausdorff Distance (HD) which yielded to a DSC of 82.99+/-5.03% and a HD of 18.91+/-7.2 voxel, respectively. In addition, the times have been measured during the manual and the GrowCut segmentations, showing that a GrowCut-segmentation - with an average time of less than six minutes (5.77+/-0.73) - is significantly shorter than a pure manual outlining.

Includes members:
  Member "interactive volumetry of liver ablation zones":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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")
  Member "refinement cut user guided segmentation algorithm for translational science":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "clinical evaluation of semi automatic open source algorithmic software segmentation of the mandibular bone practical feasibility and assessment of a new course of action")
  Member "development of an open source software module for enhanced visualization during mr guided interstitial gynecologic brachytherapy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "interactive and scale invariant segmentation of the rectum sigmoid via user defined templates")
  Member "cube cut vertebral body segmentation in mri data through cubic shaped divergences":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "computed tomography data collection of the complete human mandible and valid clinical ground truth models")
  Member "gbm volumetry using the 3d slicer medical image computing platform":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "computed tomography data collection of the complete human mandible and valid clinical ground truth models")
  Member "integration of the openigtlink network protocol for image guided therapy with the medical platform mevislab":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "computed tomography data collection of the complete human mandible and valid clinical ground truth models")
  Member "image guided therapy system for interstitial gynecologic brachytherapy in a multimodality operating suite":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "interactive and scale invariant segmentation of the rectum sigmoid via user defined templates")
  Member "template cut a pattern based segmentation paradigm":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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")

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)