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: "medical image retrieval using deep convolutional neural network"
  Abstract: Abstract   With a widespread use of digital imaging data in hospitals, the size of medical image repositories is increasing rapidly. This causes difficulty in managing and querying these large databases leading to the need of content based medical image retrieval (CBMIR) systems. A major challenge in CBMIR systems is the semantic gap that exists between the low level visual information captured by imaging devices and high level semantic information perceived by human. The efficacy of such systems is more crucial in terms of feature representations that can characterize the high-level information completely. In this paper, we propose a framework of deep learning for CBMIR system by using deep convolutional neural network (CNN) that is trained for classification of medical images. An intermodal dataset that contains twenty-four classes and five modalities is used to train the network. The learned features and the classification results are used to retrieve medical images. For retrieval, best results are achieved when class based predictions are used. An average classification accuracy of 99.77% and a mean average precision of 0.69 is achieved for retrieval task. The proposed method is best suited to retrieve multimodal medical images for different body organs.

Connected through hyperedge HE-1 (induced by source paper "secure and robust machine learning for healthcare a survey"):
  with members:
    - "securing future autonomous connected vehicles challenges posed by adversarial machine learning and the way forward"
    - "reproducibility in machine learning for health"
    - "fairness in algorithmic decision making an excursion through the lens of causality"
    - "caveat emptor the risks of using big data for human development"
    - "motion corrected multishot mri reconstruction using generative networks with sensitivity encoding"
    - "the adversarial machine learning conundrum can the insecurity of ml become the achilles heel of cognitive networks"
    - "self supervised domain adaptation for computer vision tasks"
    - "feature robustness in non stationary health records caveats to deployable model performance in common clinical machine learning tasks"

Connected through hyperedge HE-2 (induced by source paper "cumulative link models for deep ordinal classification"):
  with members:
    - "deep ordinal regression network for monocular depth estimation"
    - "automatic handgun detection alarm in videos using deep learning"
    - "deep ordinal ranking for multi category diagnosis of alzheimer s disease using hippocampal mri data"
    - "face detection using deep learning an improved faster rcnn approach"
    - "deep feature based contextual model for object detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "cyclical learning rates for training neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "a cnn rnn architecture for multi label weather recognition"):
  with members:
    - "squeeze and excitation networks"
    - "massive exploration of neural machine translation architectures"
    - "mask r cnn"
    - "deep residual learning for image recognition"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "medical image analysis using convolutional neural networks a review"):
  with members:
    - "unet a nested u net architecture for medical image segmentation"
    - "deep multi scale location aware 3d convolutional neural networks for automated detection of lacunes of presumed vascular origin"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "brain tumor segmentation with deep neural networks"
    - "adversarial discriminative domain adaptation"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "joint sequence learning and cross modality convolution for 3d biomedical segmentation"
    - "geometric convolutional neural network for analyzing surface based neuroimaging data"

Connected through hyperedge HE-5 (induced by source paper "montage based 3d medical image retrieval from traumatic brain injury cohort using deep convolutional neural network"):
  with members:
    - "a data colocation grid framework for big data medical image processing backend design"
    - "learning deep representations of medical images using siamese cnns with application to content based image retrieval"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "brain tumor image retrieval via multitask learning"):
  with members:
    - "learning deep representations of medical images using siamese cnns with application to content based image retrieval"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "deep residual learning for image recognition"
    - "fast r cnn"

Connected through hyperedge HE-7 (induced by source paper "cisrdcnn super resolution of compressed images using deep convolutional neural networks"):
  with members:
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "dehazenet an end to end system for single image haze removal"
    - "accelerating the super resolution convolutional neural network"
    - "learning a no reference quality metric for single image super resolution"
    - "accurate image super resolution using very deep convolutional networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "compression artifacts reduction by a deep convolutional network"

Connected through hyperedge HE-8 (induced by source paper "segmentation of glioma tumors in brain using deep convolutional neural network"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "patch based convolutional neural network for whole slide tissue image classification"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "practical recommendations for gradient based training of deep architectures"
    - "representation learning a review and new perspectives"

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)