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: "grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy"
  Abstract: Purpose  Use adjudication to quantify errors in diabetic retinopathy (DR) grading based on individual graders and majority decision, and to train an improved automated algorithm for DR grading.  Design  Retrospective analysis.  Participants  Retinal fundus images from DR screening programs.  Methods  Images were each graded by the algorithm, U.S. board-certified ophthalmologists, and retinal specialists. The adjudicated consensus of the retinal specialists served as the reference standard.  Main Outcome Measures  For agreement between different graders as well as between the graders and the algorithm, we measured the (quadratic-weighted) kappa score. To compare the performance of different forms of manual grading and the algorithm for various DR severity cutoffs (e.g., mild or worse DR, moderate or worse DR), we measured area under the curve (AUC), sensitivity, and specificity.  Results  Of the 193 discrepancies between adjudication by retinal specialists and majority decision of ophthalmologists, the most common were missing microaneurysm (MAs) (36%), artifacts (20%), and misclassified hemorrhages (16%). Relative to the reference standard, the kappa for individual retinal specialists, ophthalmologists, and algorithm ranged from 0.82 to 0.91, 0.80 to 0.84, and 0.84, respectively. For moderate or worse DR, the majority decision of ophthalmologists had a sensitivity of 0.838 and specificity of 0.981. The algorithm had a sensitivity of 0.971, specificity of 0.923, and AUC of 0.986. For mild or worse DR, the algorithm had a sensitivity of 0.970, specificity of 0.917, and AUC of 0.986. By using a small number of adjudicated consensus grades as a tuning dataset and higher-resolution images as input, the algorithm improved in AUC from 0.934 to 0.986 for moderate or worse DR.  Conclusions  Adjudication reduces the errors in DR grading. A small set of adjudicated DR grades allows substantial improvements in algorithm performance. The resulting algorithm's performance was on par with that of individual U.S. Board-Certified ophthalmologists and retinal specialists.

Connected through hyperedge HE-1 (induced by source paper "deep learning approach to diabetic retinopathy detection"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "on the variance of the adaptive learning rate and beyond"
    - "transfer learning based detection of diabetic retinopathy from small dataset"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "on first order meta learning algorithms"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "deep learning based computer aided diagnosis systems for diabetic retinopathy a survey"
    - "focal loss for dense object detection"

Connected through hyperedge HE-2 (induced by source paper "deepseenet a deep learning model for automated classification of patient based age related macular degeneration severity from color fundus photographs"):
  with members:
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "intelligent word embeddings of free text radiology reports"
    - "understanding neural networks through deep visualization"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "rethinking the inception architecture for computer vision"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "detecting anemia from retinal fundus images"):
  with members:
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "deep learning for predicting refractive error from retinal fundus images"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-4 (induced by source paper "reproduction study using public data of development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs"):
  with members:
    - "a survey on deep learning in medical image analysis"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "rethinking the inception architecture for computer vision"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-5 (induced by source paper "predicting progression of age related macular degeneration from fundus images using deep learning"):
  with members:
    - "deepseenet a deep learning model for automated classification of patient based age related macular degeneration severity from color fundus photographs"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-6 (induced by source paper "direct uncertainty prediction for medical second opinions"):
  with members:
    - "deep learning is robust to massive label noise"
    - "a probabilistic u net for segmentation of ambiguous images"
    - "smoothgrad removing noise by adding noise"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "axiomatic attribution for deep networks"
    - "who said what modeling individual labelers improves classification"
    - "bayesian image quality transfer with cnns exploring uncertainty in dmri super resolution"
    - "what uncertainties do we need in bayesian deep learning for computer vision"

Connected through hyperedge HE-7 (induced by source paper "phronesis of ai in radiology superhuman meets natural stupidity"):
  with members:
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"

Connected through hyperedge HE-8 (induced by source paper "deep learning and glaucoma specialists the relative importance of optic disc features to predict glaucoma referral in fundus photos"):
  with members:
    - "popular ensemble methods an empirical study"

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)