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: "medical image classification via svm using lbp features from saliency based folded data"
  Abstract: Good results on image classification and retrieval using support vector machines (SVM) with local binary patterns (LBPs) as features have been extensively reported in the literature where an entire image is retrieved or classified. In contrast, in medical imaging, not all parts of the image may be equally significant or relevant to the image retrieval application at hand. For instance, in lung x-ray image, the lung region may contain a tumour, hence being highly significant whereas the surrounding area does not contain significant information from medical diagnosis perspective. In this paper, we propose to detect salient regions of images during training and fold the data to reduce the effect of irrelevant regions. As a result, smaller image areas will be used for LBP features calculation and consequently classification by SVM. We use IRMA 2009 dataset with 14,410 xray images to verify the performance of the proposed approach. The results demonstrate the benefits of saliency-based folding approach that delivers comparable classification accuracies with state-of-the-art but exhibits lower computational cost and storage requirements, factors highly important for big data analytics.

Connected through hyperedge HE-1 (induced by source paper "subtractive perceptrons for learning images a preliminary report"):
  with members:
    - "gated graph sequence neural networks"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "convolutional networks on graphs for learning molecular fingerprints"
    - "spectral networks and locally connected networks on graphs"
    - "representation learning a review and new perspectives"
    - "multi column deep neural networks for image classification"

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"
    - "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"
    - "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"
    - "radon features and barcodes for medical image retrieval via svm"
    - "barcode annotations for medical image retrieval a preliminary investigation"

Connected through hyperedge HE-4 (induced by source paper "face recognition using scattering convolutional network"):
  with members:
    - "masked signal decomposition using subspace representation and its applications"
    - "person re identification using visual attention"
    - "screen content image segmentation using robust regression and sparse decomposition"
    - "palmprint recognition using deep scattering convolutional network"
    - "iris recognition using scattering transform and textural features"
    - "fingerprint recognition using translation invariant scattering network"
    - "a bayesian framework for sparse representation based 3 d human pose estimation"
    - "highly accurate multispectral palmprint recognition using statistical and wavelet features"

Connected through hyperedge HE-5 (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"
    - "barcode annotations for medical image retrieval a preliminary investigation"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "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-6 (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"
    - "barcode annotations for medical image retrieval a preliminary investigation"
    - "autoencoding the retrieval relevance of medical images"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "minmax radon barcodes for medical image retrieval"):
  with members:
    - "tumour roi estimation in ultrasound images via radon barcodes in patients with locally advanced breast cancer"
    - "barcode annotations for medical image retrieval a preliminary investigation"
    - "autoencoding the retrieval relevance of medical images"

Connected through hyperedge HE-8 (induced by source paper "evolutionary projection selection for radon barcodes"):
  with members:
    - "tumour roi estimation in ultrasound images via radon barcodes in patients with locally advanced breast cancer"
    - "barcode annotations for medical image retrieval a preliminary investigation"
    - "autoencoding the retrieval relevance of medical images"

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)