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 "learning autoencoded radon projections"):
  Abstract: Autoencoders have been recently used for encoding medical images. In this study, we design and validate a new framework for retrieving medical images by classifying Radon projections, compressed in the deepest layer of an autoencoder. As the autoencoder reduces the dimensionality, a multilayer perceptron (MLP) can be employed to classify the images. The integration of MLP promotes a rather shallow learning architecture which makes the training faster. We conducted a comparative study to examine the capabilities of autoencoders for different inputs such as raw images, Histogram of Oriented Gradients (HOG) and normalized Radon projections. Our framework is benchmarked on IRMA dataset containing $14,410$ x-ray images distributed across $57$ different classes. Experiments show an IRMA error of $313$ (equivalent to $\approx 82\%$ accuracy) outperforming state-of-the-art works on retrieval from IRMA dataset using autoencoders.

Includes members:
  Member "minmax radon barcodes for medical image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "projectron a shallow and interpretable network for classifying medical images")
  Member "binary codes for tagging x ray images via deep de noising autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "combining real valued and binary gabor radon features for classification and search in medical imaging archives")
  Member "barcodes for medical image retrieval using autoencoded radon transform":
    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 "stacked autoencoders for medical image search":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient video indexing for monitoring disease activity and progression in the upper gastrointestinal tract")
  Member "barcode annotations for medical image retrieval a preliminary investigation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "subtractive perceptrons for learning images a preliminary report")
  Member "autoencoding the retrieval relevance of medical images":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "medical image retrieval using deep convolutional neural network")

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)