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: "detecting hip fractures with radiologist level performance using deep neural networks"
  Abstract: We developed an automated deep learning system to detect hip fractures from frontal pelvic x-rays, an important and common radiological task. Our system was trained on a decade of clinical x-rays (~53,000 studies) and can be applied to clinical data, automatically excluding inappropriate and technically unsatisfactory studies. We demonstrate diagnostic performance equivalent to a human radiologist and an area under the ROC curve of 0.994. Translated to clinical practice, such a system has the potential to increase the efficiency of diagnosis, reduce the need for expensive additional testing, expand access to expert level medical image interpretation, and improve overall patient outcomes.

Connected through hyperedge HE-1 (induced by source paper "a review of challenges and opportunities in machine learning for health"):
  with members:
    - "feature robustness in non stationary health records caveats to deployable model performance in common clinical machine learning tasks"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "adversarial attacks against medical deep learning systems"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "scalable and accurate deep learning for electronic health records"
    - "towards vision based smart hospitals a system for tracking and monitoring hand hygiene compliance"
    - "poincar e embeddings for learning hierarchical representations"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-2 (induced by source paper "hidden stratification causes clinically meaningful failures in machine learning for medical imaging"):
  with members:
    - "slice based learning a programming model for residual learning in critical data slices"
    - "deep learning predicts hip fracture using confounding patient and healthcare variables"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "variable generalization performance of a deep learning model to detect pneumonia in chest radiographs a cross sectional study"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised universal fracture detection in pelvic x rays"):
  with members:
    - "deep learning predicts hip fracture using confounding patient and healthcare variables"
    - "weakly supervised localization and classification of proximal femur fractures"
    - "deep nets what have they ever done for vision"
    - "weakly supervised medical diagnosis and localization from multiple resolutions"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "deep learning in radiology an overview of the concepts and a survey of the state of the art"):
  with members:
    - "segmentation of the proximal femur from mr images using deep convolutional neural networks"
    - "real time automatic fetal brain extraction in fetal mri by deep learning"
    - "automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "brain tumor segmentation with deep neural networks"
    - "an analysis of deep neural network models for practical applications"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "focal loss for dense object detection"

Connected through hyperedge HE-5 (induced by source paper "adversarial attacks against medical deep learning systems"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "a dual approach to scalable verification of deep networks"
    - "adversarial patch"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "adversarial logit pairing"
    - "generalizability vs robustness adversarial examples for medical imaging"
    - "certified defenses against adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "auto classification of retinal diseases in the limit of sparse data using a two streams machine learning model"):
  with members:
    - "a novel hybrid machine learning model for auto classification of retinal diseases"
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "a novel framework for robustness analysis of visual qa models"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "radnet radiologist level accuracy using deep learning for hemorrhage detection in ct scans"
    - "vqabq visual question answering by basic questions"
    - "robustness analysis of visual qa models by basic questions"

Connected through hyperedge HE-7 (induced by source paper "deep learning predicts hip fracture using confounding patient and healthcare variables"):
  with members:
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "learning to diagnose from scratch by exploiting dependencies among labels"
    - "variable generalization performance of a deep learning model to detect pneumonia in chest radiographs a cross sectional study"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "abnormality detection and localization in chest x rays using deep convolutional neural networks"
    - "fast and accurate classification of echocardiograms using deep learning"
    - "gender from iris or gender from mascara"
    - "semantics derived automatically from language corpora contain human like biases"

Connected through hyperedge HE-8 (induced by source paper "towards generative adversarial networks as a new paradigm for radiology education"):
  with members:
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "high resolution medical image synthesis using progressively grown generative adversarial networks"
    - "image synthesis in multi contrast mri with conditional generative adversarial networks"
    - "high resolution mammogram synthesis using progressive generative adversarial networks"
    - "freehand ultrasound image simulation with spatially conditioned generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "a classification based study of covariate shift in gan distributions"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"

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)