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: "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.

Connected through hyperedge HE-1 (induced by source paper "pituitary adenoma volumetry with 3d slicer"):
  with members:
    - "template cut a pattern based segmentation paradigm"
    - "pituitary adenoma segmentation"

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)