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: "fast and accurate classification of echocardiograms using deep learning"
  Abstract: Echocardiography is essential to modern cardiology. However, human interpretation limits high throughput analysis, limiting echocardiography from reaching its full clinical and research potential for precision medicine. Deep learning is a cutting-edge machine-learning technique that has been useful in analyzing medical images but has not yet been widely applied to echocardiography, partly due to the complexity of echocardiograms' multi view, multi modality format. The essential first step toward comprehensive computer assisted echocardiographic interpretation is determining whether computers can learn to recognize standard views. To this end, we anonymized 834,267 transthoracic echocardiogram (TTE) images from 267 patients (20 to 96 years, 51 percent female, 26 percent obese) seen between 2000 and 2017 and labeled them according to standard views. Images covered a range of real world clinical variation. We built a multilayer convolutional neural network and used supervised learning to simultaneously classify 15 standard views. Eighty percent of data used was randomly chosen for training and 20 percent reserved for validation and testing on never seen echocardiograms. Using multiple images from each clip, the model classified among 12 video views with 97.8 percent overall test accuracy without overfitting. Even on single low resolution images, test accuracy among 15 views was 91.7 percent versus 70.2 to 83.5 percent for board-certified echocardiographers. Confusional matrices, occlusion experiments, and saliency mapping showed that the model finds recognizable similarities among related views and classifies using clinically relevant image features. In conclusion, deep neural networks can classify essential echocardiographic views simultaneously and with high accuracy. Our results provide a foundation for more complex deep learning assisted echocardiographic interpretation.

Connected through hyperedge HE-1 (induced by source paper "pznet efficient 3d convnet inference on manycore cpus"):
  with members:
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "a survey on deep learning in medical image analysis"
    - "fusionnet a deep fully residual convolutional neural network for image segmentation in connectomics"
    - "the importance of skip connections in biomedical image segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-2 (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"
    - "gender from iris or gender from mascara"
    - "semantics derived automatically from language corpora contain human like biases"
    - "detecting hip fractures with radiologist level performance using deep neural 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)