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: "radiomics strategies for risk assessment of tumour failure in head and neck cancer"
  Abstract: Quantitative extraction of high-dimensional mineable data from medical images is a process known as radiomics. Radiomics is foreseen as an essential prognostic tool for cancer risk assessment and the quantification of intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET and CT images of 300 patients from four different cohorts were analyzed for the risk assessment of locoregional recurrences (LR) and distant metastases (DM) in head-and-neck cancer. Prediction models combining radiomic and clinical variables were constructed via random forests and imbalance-adjustment strategies using two of the four cohorts. Independent validation of the prediction and prognostic performance of the models was carried out on the other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88). Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the potential of radiomics for assessing the risk of specific tumour outcomes using multiple stratification groups. This could have important clinical impact, notably by allowing for a better personalization of chemo-radiation treatments for head-and-neck cancer patients from different risk groups.

Connected through hyperedge HE-1 (induced by source paper "deep learning for automated medical image analysis"):
  with members:
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy"
    - "deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy"
    - "deepem deep 3d convnets with em for weakly supervised pulmonary nodule detection"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "3d segmentation with exponential logarithmic loss for highly unbalanced object sizes"
    - "focal loss for dense object detection"

Connected through hyperedge HE-2 (induced by source paper "mining domain knowledge improved framework towards automatically standardizing anatomical structure nomenclature in radiotherapy"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy"
    - "benchmark analysis of representative deep neural network architectures"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "bagan data augmentation with balancing gan"
    - "autoaugment learning augmentation policies from data"
    - "non local neural networks"
    - "focal loss for dense object detection"

Connected through hyperedge HE-3 (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"
    - "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"
    - "towards automatic pulmonary nodule management in lung cancer screening with deep learning"

Connected through hyperedge HE-4 (induced by source paper "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy"):
  with members:
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "3d segmentation with exponential logarithmic loss for highly unbalanced object sizes"
    - "focal loss for dense object detection"
    - "adversarial deep structured nets for mass segmentation from mammograms"
    - "squeeze and excitation networks"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "deeplung 3d deep convolutional nets for automated pulmonary nodule detection and classification"
    - "tversky loss function for image segmentation using 3d fully convolutional deep networks"

Connected through hyperedge HE-5 (induced by source paper "atlas based segmentations via semi supervised diffeomorphic registrations"):
  with members:
    - "learning a probabilistic model for diffeomorphic registration"
    - "unsupervised learning of probabilistic diffeomorphic registration for images and surfaces"
    - "voxelmorph a learning framework for deformable medical image registration"
    - "unsupervised 3d end to end medical image registration with volume tweening network"
    - "deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy"
    - "infovae information maximizing variational autoencoders"
    - "tensorflow a system for large scale machine learning"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-6 (induced by source paper "assessing robustness of radiomic features by image perturbation"):
  with members:
    - "image biomarker standardisation initiative"
    - "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-7 (induced by source paper "unsupervised video to video translation"):
  with members:
    - "cycada cycle consistent adversarial domain adaptation"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "playing for data ground truth from computer games"
    - "generating videos with scene dynamics"
    - "deep multi scale video prediction beyond mean square error"

Connected through hyperedge HE-8 (induced by source paper "image biomarker standardisation initiative"):
  with members:
    - "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"

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)