human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "combining real valued and binary gabor radon features for classification and search in medical imaging archives"):
  Abstract: Content-based image retrieval (CBIR) of medical images in large datasets to identify similar images when a query image is given can be very useful in improving the diagnostic decision of the clinical experts and as well in educational scenarios. In this paper, we used two stage classification and retrieval approach to retrieve similar images. First, the Gabor filters are applied to Radon-transformed images to extract features and to train a multi-class SVM. Then based on the classification results and using an extracted Gabor barcode, similar images are retrieved. The proposed method was tested on IRMA dataset which contains more than 14,000 images. Experimental results show the efficiency of our approach in retrieving similar images compared to other Gabor-Radon-oriented methods.

Includes members:
  Member "binary codes for tagging x ray images via deep de noising autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning autoencoded radon projections")
  Member "generating binary tags for fast medical image retrieval based on convolutional nets and radon transform":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on biomedical image captioning")
  Member "gabor barcodes for medical image retrieval":
    also connected through other hyperedges:
      (none)
  Member "radon gabor barcodes for medical image retrieval":
    also connected through other hyperedges:
      (none)
  Member "radon features and barcodes for medical image retrieval via svm":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "projectron a shallow and interpretable network for classifying medical images")
  Member "barcode annotations for medical image retrieval a preliminary investigation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "projectron a shallow and interpretable network for classifying medical images")
  Member "medical image classification via svm using lbp features from saliency based folded data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "subtractive perceptrons for learning images a preliminary report")

Question: Please tell me which class the source paper 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)