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: "tumour roi estimation in ultrasound images via radon barcodes in patients with locally advanced breast cancer"
  Abstract: Quantitative ultrasound (QUS) methods provide a promising framework that can non-invasively and inexpensively be used to predict or assess the tumour response to cancer treatment. The first step in using the QUS methods is to select a region of interest (ROI) inside the tumour in ultrasound images. Manual segmentation, however, is very time consuming and tedious. In this paper, a semi-automated approach will be proposed to roughly localize an ROI for a tumour in ultrasound images of patients with locally advanced breast cancer (LABC). Content-based barcodes, a recently introduced binary descriptor based on Radon transform, were used in order to find similar cases and estimate a bounding box surrounding the tumour. Experiments with 33 B-scan images resulted in promising results with an accuracy of $81\%$.

Connected through hyperedge HE-1 (induced by source paper "binary codes for tagging x ray images via deep de noising autoencoders"):
  with members:
    - "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"
    - "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-2 (induced by source paper "generating binary tags for fast medical image retrieval based on convolutional nets and radon transform"):
  with members:
    - "barcode annotations for medical image retrieval a preliminary investigation"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "minmax radon barcodes for medical image retrieval"):
  with members:
    - "barcode annotations for medical image retrieval a preliminary investigation"
    - "medical image classification via svm using lbp features from saliency based folded data"
    - "autoencoding the retrieval relevance of medical images"

Connected through hyperedge HE-4 (induced by source paper "evolutionary projection selection for radon barcodes"):
  with members:
    - "barcode annotations for medical image retrieval a preliminary investigation"
    - "medical image classification via svm using lbp features from saliency based folded data"
    - "autoencoding the retrieval relevance of medical images"

Connected through hyperedge HE-5 (induced by source paper "radon features and barcodes for medical image retrieval via svm"):
  with members:
    - "barcode annotations for medical image retrieval a preliminary investigation"
    - "medical image classification via svm using lbp features from saliency based folded data"
    - "autoencoding the retrieval relevance of medical images"

Connected through hyperedge HE-6 (induced by source paper "barcodes for medical image retrieval using autoencoded radon transform"):
  with members:
    - "barcode annotations for medical image retrieval a preliminary investigation"
    - "autoencoding the retrieval relevance of medical images"

Connected through hyperedge HE-7 (induced by source paper "gabor barcodes for medical image retrieval"):
  with members:
    - "barcode annotations for medical image retrieval a preliminary investigation"

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)