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 "refinement cut user guided segmentation algorithm for translational science"):
  Abstract: In this contribution, a semi-automatic segmentation algorithm for (medical) image analysis is presented. More precise, the approach belongs to the category of interactive contouring algorithms, which provide real-time feedback of the segmentation result. However, even with interactive real-time contouring approaches there are always cases where the user cannot find a satisfying segmentation, e.g. due to homogeneous appearances between the object and the background, or noise inside the object. For these difficult cases the algorithm still needs additional user support. However, this additional user support should be intuitive and rapid integrated into the segmentation process, without breaking the interactive real-time segmentation feedback. I propose a solution where the user can support the algorithm by an easy and fast placement of one or more seed points to guide the algorithm to a satisfying segmentation result also in difficult cases. These additional seed(s) restrict(s) the calculation of the segmentation for the algorithm, but at the same time, still enable to continue with the interactive real-time feedback segmentation. For a practical and genuine application in translational science, the approach has been tested on medical data from the clinical routine in 2D and 3D.

Includes members:
  Member "cube cut vertebral body segmentation in mri data through cubic shaped divergences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "computed tomography data collection of the complete human mandible and valid clinical ground truth models")
  Member "pcg cut graph driven segmentation of the prostate central gland":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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 "image guided therapy system for interstitial gynecologic brachytherapy in a multimodality operating suite":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (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 "pituitary adenoma volumetry with 3d slicer":
    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 "square cut a segmentation algorithm on the basis of a rectangle shape":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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 "a flexible semi automatic approach for glioblastoma multiforme segmentation":
    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)