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: "deep convolutional networks for automated detection of posterior element fractures on spine ct"
  Abstract: Injuries of the spine, and its posterior elements in particular, are a common occurrence in trauma patients, with potentially devastating consequences. Computer-aided detection (CADe) could assist in the detection and classification of spine fractures. Furthermore, CAD could help assess the stability and chronicity of fractures, as well as facilitate research into optimization of treatment paradigms. In this work, we apply deep convolutional networks (ConvNets) for the automated detection of posterior element fractures of the spine. First, the vertebra bodies of the spine with its posterior elements are segmented in spine CT using multi-atlas label fusion. Then, edge maps of the posterior elements are computed. These edge maps serve as candidate regions for predicting a set of probabilities for fractures along the image edges using ConvNets in a 2.5D fashion (three orthogonal patches in axial, coronal and sagittal planes). We explore three different methods for training the ConvNet using 2.5D patches along the edge maps of `positive', i.e. fractured posterior-elements and `negative', i.e. non-fractured elements. An experienced radiologist retrospectively marked the location of 55 displaced posterior-element fractures in 18 trauma patients. We randomly split the data into training and testing cases. In testing, we achieve an area-under-the-curve of 0.857. This corresponds to 71% or 81% sensitivities at 5 or 10 false-positives per patient, respectively. Analysis of our set of trauma patients demonstrates the feasibility of detecting posterior-element fractures in spine CT images using computer vision techniques such as deep convolutional networks.

Connected through hyperedge HE-1 (induced by source paper "feature learning to automatically assess radiographic knee osteoarthritis severity"):
  with members:
    - "automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks"
    - "cnn based segmentation of medical imaging data"
    - "brain tumor segmentation with deep neural networks"
    - "automatic knee osteoarthritis diagnosis from plain radiographs a deep learning based approach"
    - "automatic detection of knee joints and quantification of knee osteoarthritis severity using convolutional neural networks"
    - "a novel method for automatic localization of joint area on knee plain radiographs"
    - "a survey on deep learning in medical image analysis"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "towards an interactive and interpretable cad system to support proximal femur fracture classification"):
  with members:
    - "weakly supervised localization and classification of proximal femur fractures"
    - "understanding anatomy classification through attentive response maps"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "visualizing and understanding convolutional networks"

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 "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-5 (induced by source paper "weakly supervised localization and classification of proximal femur fractures"):
  with members:
    - "compression fractures detection on ct"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "self transfer learning for fully weakly supervised object localization"
    - "deep residual learning for image recognition"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "learning for biomedical information extraction methodological review of recent advances"):
  with members:
    - "implicit distortion and fertility models for attention based encoder decoder nmt model"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "attention based convolutional neural network for machine comprehension"
    - "improved relation classification by deep recurrent neural networks with data augmentation"
    - "toward mention detection robustness with recurrent neural networks"
    - "multi way multilingual neural machine translation with a shared attention mechanism"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "combining neural networks and log linear models to improve relation extraction"

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)