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: "image biomarker standardisation initiative"
  Abstract: The image biomarker standardisation initiative (IBSI) is an independent international collaboration which works towards standardising the extraction of image biomarkers from acquired imaging for the purpose of high-throughput quantitative image analysis (radiomics). Lack of reproducibility and validation of high-throughput quantitative image analysis studies is considered to be a major challenge for the field. Part of this challenge lies in the scantiness of consensus-based guidelines and definitions for the process of translating acquired imaging into high-throughput image biomarkers. The IBSI therefore seeks to provide image biomarker nomenclature and definitions, benchmark data sets, and benchmark values to verify image processing and image biomarker calculations, as well as reporting guidelines, for high-throughput image analysis.

Connected through hyperedge HE-1 (induced by source paper "from handcrafted to deep learning based cancer radiomics challenges and opportunities"):
  with members:
    - "semantic segmentation of pathological lung tissue with dilated fully convolutional networks"
    - "brain tumor type classification via capsule networks"
    - "dynamic routing between capsules"
    - "radiomics strategies for risk assessment of tumour failure in head and neck cancer"
    - "prostate cancer diagnosis using deep learning with 3d multiparametric mri"
    - "understanding convolution for semantic segmentation"
    - "a survey on deep learning in medical image analysis"
    - "discovery radiomics via evolutionary deep radiomic sequencer discovery for pathologically proven lung cancer detection"

Connected through hyperedge HE-2 (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"
    - "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"
    - "scikit image image processing in python"

Connected through hyperedge HE-3 (induced by source paper "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"):
  with members:
    - "a pretrained densenet encoder for brain tumor segmentation"
    - "deep learning versus classical regression for brain tumor patient survival prediction"
    - "brain tumor segmentation and radiomics survival prediction contribution to the brats 2017 challenge"
    - "hierarchical multi class segmentation of glioma images using networks with multi level activation function"
    - "3d mri brain tumor segmentation using autoencoder regularization"
    - "brain tumor segmentation and tractographic feature extraction from structural mr images for overall survival prediction"
    - "glioma segmentation with cascaded unet"
    - "automatic brain tumor segmentation using convolutional neural networks with test time augmentation"

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)