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: "minmax radon barcodes for medical image retrieval"
  Abstract: Content-based medical image retrieval can support diagnostic decisions by clinical experts. Examining similar images may provide clues to the expert to remove uncertainties in his/her final diagnosis. Beyond conventional feature descriptors, binary features in different ways have been recently proposed to encode the image content. A recent proposal is "Radon barcodes" that employ binarized Radon projections to tag/annotate medical images with content-based binary vectors, called barcodes. In this paper, MinMax Radon barcodes are introduced which are superior to "local thresholding" scheme suggested in the literature. Using IRMA dataset with 14,410 x-ray images from 193 different classes, the advantage of using MinMax Radon barcodes over \emph{thresholded} Radon barcodes are demonstrated. The retrieval error for direct search drops by more than 15\%. As well, SURF, as a well-established non-binary approach, and BRISK, as a recent binary method are examined to compare their results with MinMax Radon barcodes when retrieving images from IRMA dataset. The results demonstrate that MinMax Radon barcodes are faster and more accurate when applied on IRMA images.

Connected through hyperedge HE-1 (induced by source paper "projectron a shallow and interpretable network for classifying medical images"):
  with members:
    - "facial recognition with encoded local projections"
    - "retrieving similar x ray images from big image data using radon barcodes with single projections"
    - "local radon descriptors for image search"
    - "learning autoencoded radon projections"
    - "radon features and barcodes for medical image retrieval via svm"
    - "barcode annotations for medical image retrieval a preliminary investigation"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-2 (induced by source paper "facial recognition with encoded local projections"):
  with members:
    - "retrieving similar x ray images from big image data using radon barcodes with single projections"
    - "local radon descriptors for image search"
    - "a comparative study of cnn bovw and lbp for classification of histopathological images"

Connected through hyperedge HE-3 (induced by source paper "convolutional neural networks for histopathology image classification training vs using pre trained networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "optimizing and visualizing deep learning for benign malignant classification in breast tumors"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "barcode annotations for medical image retrieval a preliminary investigation"
    - "rethinking the inception architecture for computer vision"
    - "very deep convolutional networks for large scale image recognition"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "learning autoencoded radon projections"):
  with members:
    - "binary codes for tagging x ray images via deep de noising autoencoders"
    - "barcodes for medical image retrieval using autoencoded radon transform"
    - "radon features and barcodes for medical image retrieval via svm"
    - "stacked autoencoders for medical image search"
    - "barcode annotations for medical image retrieval a preliminary investigation"
    - "medical image classification via svm using lbp features from saliency based folded data"
    - "autoencoding the retrieval relevance of medical images"
    - "object recognition using deep neural networks a survey"

Connected through hyperedge HE-5 (induced by source paper "local radon descriptors for image search"):
  with members:
    - "radon features and barcodes for medical image retrieval via svm"
    - "barcode annotations for medical image retrieval a preliminary investigation"

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)