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 "preoperative volume determination for pituitary adenoma"):
  Abstract: The most common sellar lesion is the pituitary adenoma, and sellar tumors are approximately 10-15% of all intracranial#R##N#neoplasms. Manual slice-by-slice segmentation takes quite some time that can be reduced by using the appropriate#R##N#algorithms. In this contribution, we present a segmentation method for pituitary adenoma. The method is based on an#R##N#algorithm that we have applied recently to segmenting glioblastoma multiforme. A modification of this scheme is used#R##N#for adenoma segmentation that is much harder to perform, due to lack of contrast-enhanced boundaries. In our#R##N#experimental evaluation, neurosurgeons performed manual slice-by-slice segmentation of ten magnetic resonance#R##N#imaging (MRI) cases. The segmentations were compared to the segmentation results of the proposed method using the#R##N#Dice Similarity Coefficient (DSC). The average DSC for all datasets was 75.92%±7.24%. A manual segmentation took#R##N#about four minutes and our algorithm required about one second.

Includes members:
  Member "glioblastoma multiforme segmentation in mri data with a balloon inflation approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gbm volumetry using the 3d slicer medical image computing platform")
  Member "a flexible semi automatic approach for glioblastoma multiforme segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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 segmentation":
    also connected through other hyperedges:
      - 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")

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)