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: "autoencoding the retrieval relevance of medical images"
  Abstract: Content-based image retrieval (CBIR) of medical images is a crucial task that can contribute to a more reliable diagnosis if applied to big data. Recent advances in feature extraction and classification have enormously improved CBIR results for digital images. However, considering the increasing accessibility of big data in medical imaging, we are still in need of reducing both memory requirements and computational expenses of image retrieval systems. This work proposes to exclude the features of image blocks that exhibit a low encoding error when learned by a $n/p/n$ autoencoder ($p\!<\!n$). We examine the histogram of autoendcoding errors of image blocks for each image class to facilitate the decision which image regions, or roughly what percentage of an image perhaps, shall be declared relevant for the retrieval task. This leads to reduction of feature dimensionality and speeds up the retrieval process. To validate the proposed scheme, we employ local binary patterns (LBP) and support vector machines (SVM) which are both well-established approaches in CBIR research community. As well, we use IRMA dataset with 14,410 x-ray images as test data. The results show that the dimensionality of annotated feature vectors can be reduced by up to 50% resulting in speedups greater than 27% at expense of less than 1% decrease in the accuracy of retrieval when validating the precision and recall of the top 20 hits.

Connected through hyperedge HE-1 (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"
    - "medical image classification via svm using lbp features from saliency based folded data"
    - "object recognition using deep neural networks a survey"

Connected through hyperedge HE-2 (induced by source paper "medical image retrieval using deep convolutional neural network"):
  with members:
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "automatic segmentation of mr brain images with a convolutional neural network"
    - "deep residual learning for image recognition"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "visualizing and understanding convolutional networks"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-3 (induced by source paper "relational autoencoder for feature extraction"):
  with members:
    - "learning in high dimensional multimedia data the state of the art"
    - "deep feature learning for eeg recordings"

Connected through hyperedge HE-4 (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"
    - "medical image classification via svm using lbp features from saliency based folded data"
    - "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-5 (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"
    - "medical image classification via svm using lbp features from saliency based folded data"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (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"
    - "medical image classification via svm using lbp features from saliency based folded data"

Connected through hyperedge HE-7 (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"
    - "medical image classification via svm using lbp features from saliency based folded data"

Connected through hyperedge HE-8 (induced by source paper "radon features and barcodes for medical image retrieval via svm"):
  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"
    - "medical image classification via svm using lbp features from saliency based folded data"

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)