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 "reversed active learning based atrous densenet for pathological image classification"):
  Abstract: Witnessed the development of deep learning in recent years, increasing number of researches try to adopt deep learning model for medical image analysis. However, the usage of deep learning networks for the pathological image analysis encounters several challenges, e.g. high resolution (gigapixel) of pathological images and lack of annotations of cancer areas. To address the challenges, we proposed a complete framework for the pathological image classification, which consists of a novel training strategy, namely reversed active learning (RAL), and an advanced network, namely atrous DenseNet (ADN). The proposed RAL can remove the mislabel patches in the training set. The refined training set can then be used to train widely used deep learning networks, e.g. VGG-16, ResNets, etc. A novel deep learning network, i.e. atrous DenseNet (ADN), is also proposed for the classification of pathological images. The proposed ADN achieves multi-scale feature extraction by integrating the atrous convolutions to the Dense Block. The proposed RAL and ADN have been evaluated on two pathological datasets, i.e. BACH and CCG. The experimental results demonstrate the excellent performance of the proposed ADN + RAL framework, i.e. the average patch-level ACAs of 94.10% and 92.05% on BACH and CCG validation sets were achieved.

Includes members:
  Member "an end to end deep learning histochemical scoring system for breast cancer tma":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fuzzy semantic segmentation of breast ultrasound image with breast anatomy constraints")
  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 "suggestive annotation a deep active learning framework for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reinforced active learning for image segmentation")
  Member "rethinking atrous convolution for semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "partial labeled gastric tumor segmentation via patch based reiterative learning":
    also connected through other hyperedges:
      (none)
  Member "deep learning assessment of tumor proliferation in breast cancer histological images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an end to end deep learning histochemical scoring system for breast cancer tma")
  Member "wide residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning representations by predicting bags of visual words")
  Member "dcan deep contour aware networks for accurate gland segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bi directional dermoscopic feature learning and multi scale consistent decision fusion for skin lesion segmentation")

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)