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: "radon features and barcodes for medical image retrieval via svm"
  Abstract: For more than two decades, research has been performed on content-based image retrieval (CBIR). By combining Radon projections and the support vector machines (SVM), a content-based medical image retrieval method is presented in this work. The proposed approach employs the normalized Radon projections with corresponding image category labels to build an SVM classifier, and the Radon barcode database which encodes every image in a binary format is also generated simultaneously to tag all images. To retrieve similar images when a query image is given, Radon projections and the barcode of the query image are generated. Subsequently, the k-nearest neighbor search method is applied to find the images with minimum Hamming distance of the Radon barcode within the same class predicted by the trained SVM classifier that uses Radon features. The performance of the proposed method is validated by using the IRMA 2009 dataset with 14,410 x-ray images in 57 categories. The results demonstrate that our method has the capacity to retrieve similar responses for the correctly identified query image and even for those mistakenly classified by SVM. The approach further is very fast and has low memory requirement.

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"
    - "minmax radon barcodes for medical image retrieval"
    - "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 "learning autoencoded radon projections"):
  with members:
    - "minmax radon barcodes for medical image retrieval"
    - "binary codes for tagging x ray images via deep de noising autoencoders"
    - "barcodes for medical image retrieval using autoencoded radon transform"
    - "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-3 (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"
    - "generating binary tags for fast medical image retrieval based on convolutional nets and radon transform"
    - "gabor barcodes for medical image retrieval"
    - "radon gabor barcodes for medical image retrieval"
    - "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-4 (induced by source paper "local radon descriptors for image search"):
  with members:
    - "minmax radon barcodes for medical image retrieval"
    - "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)