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: "generating binary tags for fast medical image retrieval based on convolutional nets and radon transform"
  Abstract: Content-based image retrieval (CBIR) in large medical image archives is a challenging and necessary task. Generally, different feature extraction methods are used to assign expressive and invariant features to each image such that the search for similar images comes down to feature classification and/or matching. The present work introduces a new image retrieval method for medical applications that employs a convolutional neural network (CNN) with recently introduced Radon barcodes. We combine neural codes for global classification with Radon barcodes for the final retrieval. We also examine image search based on regions of interest (ROI) matching after image retrieval. The IRMA dataset with more than 14,000 x-rays images is used to evaluate the performance of our method. Experimental results show that our approach is superior to many published works.

Connected through hyperedge HE-1 (induced by source paper "a survey on biomedical image captioning"):
  with members:
    - "on the automatic generation of medical imaging reports"
    - "producing radiologist quality reports for interpretable artificial intelligence"
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "tandemnet distilling knowledge from medical images using diagnostic reports as optional semantic references"
    - "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"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-2 (induced by source paper "combining real valued and binary gabor radon features for classification and search in medical imaging archives"):
  with members:
    - "binary codes for tagging x ray images via deep de noising autoencoders"
    - "gabor barcodes for medical image retrieval"
    - "radon gabor barcodes for medical image retrieval"
    - "radon features and barcodes for medical image retrieval via svm"
    - "barcode annotations for medical image retrieval a preliminary investigation"
    - "medical image classification via svm using lbp features from saliency based folded data"

Connected through hyperedge HE-3 (induced by source paper "learning deep representations of medical images using siamese cnns with application to content based image retrieval"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "a survey on deep learning in medical image analysis"
    - "medical image synthesis with context aware generative adversarial networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

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)