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: "barcode annotations for medical image retrieval a preliminary investigation"
  Abstract: This paper proposes to generate and to use barcodes to annotate medical images and/or their regions of interest such as organs, tumors and tissue types. A multitude of efficient feature-based image retrieval methods already exist that can assign a query image to a certain image class. Visual annotations may help to increase the retrieval accuracy if combined with existing feature-based classification paradigms. Whereas with annotations we usually mean textual descriptions, in this paper barcode annotations are proposed. In particular, Radon barcodes (RBC) are introduced. As well, local binary patterns (LBP) and local Radon binary patterns (LRBP) are implemented as barcodes. The IRMA x-ray dataset with 12,677 training images and 1,733 test images is used to verify how barcodes could facilitate image retrieval.

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"
    - "radon features and barcodes for medical image retrieval via svm"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-2 (induced by source paper "the effects of image pre and post processing wavelet decomposition and local binary patterns on u nets for skin lesion segmentation"):
  with members:
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "skin lesion segmentation u nets versus clustering"
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "spatially aware melanoma segmentation using hybrid deep learning techniques"
    - "isic 2017 skin lesion analysis towards melanoma detection"
    - "automatic skin lesion analysis using large scale dermoscopy images and deep residual networks"
    - "recod titans at isic challenge 2017"
    - "skin lesion analysis toward melanoma detection a challenge at the 2017 international symposium on biomedical imaging isbi hosted by the international skin imaging collaboration isic"

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"
    - "minmax radon barcodes for medical image retrieval"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "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:
    - "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"
    - "radon features and barcodes for medical image retrieval via svm"
    - "stacked autoencoders for medical image search"
    - "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 "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"
    - "radon features and barcodes for medical image retrieval via svm"
    - "medical image classification via svm using lbp features from saliency based folded data"

Connected through hyperedge HE-6 (induced by source paper "local radon descriptors for image search"):
  with members:
    - "minmax radon barcodes for medical image retrieval"
    - "radon features and barcodes for medical image retrieval via svm"

Connected through hyperedge HE-7 (induced by source paper "binary codes for tagging x ray images via deep de noising autoencoders"):
  with members:
    - "tumour roi estimation in ultrasound images via radon barcodes in patients with locally advanced breast cancer"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "medical image classification via svm using lbp features from saliency based folded data"
    - "autoencoding the retrieval relevance of medical images"
    - "cnn based hashing for image retrieval"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"
    - "adam a method for stochastic optimization"
    - "theano new features and speed improvements"

Connected through hyperedge HE-8 (induced by source paper "generating binary tags for fast medical image retrieval based on convolutional nets and radon transform"):
  with members:
    - "tumour roi estimation in ultrasound images via radon barcodes in patients with locally advanced breast cancer"
    - "medical image classification via svm using lbp features from saliency based folded data"
    - "autoencoding the retrieval relevance of medical images"
    - "very deep convolutional networks for large scale image recognition"

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)