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: "radnet radiologist level accuracy using deep learning for hemorrhage detection in ct scans"
  Abstract: We describe a deep learning approach for automated brain hemorrhage detection from computed tomography (CT) scans. Our model emulates the procedure followed by radiologists to analyse a 3D CT scan in real-world. Similar to radiologists, the model sifts through 2D cross-sectional slices while paying close attention to potential hemorrhagic regions. Further, the model utilizes 3D context from neighboring slices to improve predictions at each slice and subsequently, aggregates the slice-level predictions to provide diagnosis at CT level. We refer to our proposed approach as Recurrent Attention DenseNet (RADnet) as it employs original DenseNet architecture along with adding the components of attention for slice level predictions and recurrent neural network layer for incorporating 3D context. The real-world performance of RADnet has been benchmarked against independent analysis performed by three senior radiologists for 77 brain CTs. RADnet demonstrates 81.82% hemorrhage prediction accuracy at CT level that is comparable to radiologists. Further, RADnet achieves higher recall than two of the three radiologists, which is remarkable.

Connected through hyperedge HE-1 (induced by source paper "medical time series classification with hierarchical attention based temporal convolutional networks a case study of myotonic dystrophy diagnosis"):
  with members:
    - "improving clinical predictions through unsupervised time series representation learning"
    - "attention based deep multiple instance learning"
    - "focusing on what is relevant time series learning and understanding using attention"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "neural attention models for sequence classification analysis and application to key term extraction and dialogue act detection"

Connected through hyperedge HE-2 (induced by source paper "improved ich classification using task dependent learning"):
  with members:
    - "textray mining clinical reports to gain a broad understanding of chest x rays"
    - "development and validation of deep learning algorithms for detection of critical findings in head ct scans"
    - "compression fractures detection on ct"
    - "mask r cnn"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (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-4 (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"
    - "vqabq visual question answering by basic questions"
    - "robustness analysis of visual qa models by basic questions"
    - "superimposition of eye fundus images for longitudinal analysis from large public health databases"

Connected through hyperedge HE-5 (induced by source paper "development and validation of deep learning algorithms for detection of critical findings in head ct scans"):
  with members:
    - "learning to diagnose from scratch by exploiting dependencies among labels"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "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"
    - "2d 3d fully convolutional neural networks for cardiac mr segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-6 (induced by source paper "deepradiologynet radiologist level pathology detection in ct head images"):
  with members:
    - "learning to diagnose from scratch by exploiting dependencies among labels"
    - "cardiologist level arrhythmia detection with 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"
    - "abnormality detection and localization in chest x rays using deep convolutional neural networks"
    - "aggregated residual transformations for deep neural networks"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "chexnet radiologist level pneumonia detection on chest x rays with deep learning"):
  with members:
    - "learning to diagnose from scratch by exploiting dependencies among labels"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "abnormality detection and localization in chest x rays using deep convolutional neural networks"
    - "densely connected convolutional networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning deep features for discriminative localization"

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)