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: "reliability of pet ct shape and heterogeneity features in functional and morphological components of non small cell lung cancer tumors a repeatability analysis in a prospective multi center cohort"
  Abstract: Purpose: The main purpose of this study was to assess the reliability of shape and heterogeneity features in both Positron Emission Tomography (PET) and low-dose Computed Tomography (CT) components of PET/CT. A secondary objective was to investigate the impact of image quantization.Material and methods: A Health Insurance Portability and Accountability Act -compliant secondary analysis of deidentified prospectively acquired PET/CT test-retest datasets of 74 patients from multi-center Merck and ACRIN trials was performed. Metabolically active volumes were automatically delineated on PET with Fuzzy Locally Adaptive Bayesian algorithm. 3DSlicerTM was used to semi-automatically delineate the anatomical volumes on low-dose CT components. Two quantization methods were considered: a quantization into a set number of bins (quantizationB) and an alternative quantization with bins of fixed width (quantizationW). Four shape descriptors, ten first-order metrics and 26 textural features were computed. Bland-Altman analysis was used to quantify repeatability. Features were subsequently categorized as very reliable, reliable, moderately reliable and poorly reliable with respect to the corresponding volume variability. Results: Repeatability was highly variable amongst features. Numerous metrics were identified as poorly or moderately reliable. Others were (very) reliable in both modalities, and in all categories (shape, 1st-, 2nd- and 3rd-order metrics). Image quantization played a major role in the features repeatability. Features were more reliable in PET with quantizationB, whereas quantizationW showed better results in CT.Conclusion: The test-retest repeatability of shape and heterogeneity features in PET and low-dose CT varied greatly amongst metrics. The level of repeatability also depended strongly on the quantization step, with different optimal choices for each modality. The repeatability of PET and low-dose CT features should be carefully taken into account when selecting metrics to build multiparametric models.

Connected through hyperedge HE-1 (induced by source paper "assessing robustness of radiomic features by image perturbation"):
  with members:
    - "radiomics strategies for risk assessment of tumour failure in head and neck cancer"
    - "image biomarker standardisation initiative"
    - "scikit image image processing in python"

Connected through hyperedge HE-2 (induced by source paper "image biomarker standardisation initiative"):
  with members:
    - "radiomics strategies for risk assessment of tumour failure in head and neck cancer"

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)