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 "deep learning framework for multi class breast cancer histology image classification"):
  Abstract: In this work, we present a deep learning framework for multi-class breast cancer image classification as our submission to the International Conference on Image Analysis and Recognition (ICIAR) 2018 Grand Challenge on BreAst Cancer Histology images (BACH). As these histology images are too large to fit into GPU memory, we first propose using Inception V3 to perform patch level classification. The patch level predictions are then passed through an ensemble fusion framework involving majority voting, gradient boosting machine (GBM), and logistic regression to obtain the image level prediction. We improve the sensitivity of the Normal and Benign predicted classes by designing a Dual Path Network (DPN) to be used as a feature extractor where these extracted features are further sent to a second layer of ensemble prediction fusion using GBM, logistic regression, and support vector machine (SVM) to refine predictions. Experimental results demonstrate our framework shows a 12.5$\%$ improvement over the state-of-the-art model.

Includes members:
  Member "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "detecting cancer metastases on gigapixel pathology images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on explainable artificial intelligence xai towards medical xai")
  Member "adversarial deep structured nets for mass segmentation from mammograms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "end to end face parsing via interlinked convolutional neural networks")
  Member "the importance of stain normalization in colorectal tissue classification with convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology")
  Member "dual path networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "convolutional networks with dense connectivity")
  Member "deep multi instance networks with sparse label assignment for whole mammogram classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an interpretable classifier for high resolution breast cancer screening images utilizing weakly supervised localization")
  Member "patch based convolutional neural network for whole slide tissue image classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gastric histopathology image segmentation using a hierarchical conditional random field")
  Member "going deeper with convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)