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 "machine learning methods for histopathological image analysis"):
  Abstract: Abstract   Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to be considered. In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions.

Includes members:
  Member "convolutional neural networks for histopathology image classification training vs using pre trained networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improving prostate cancer detection with breast histopathology images")
  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 "the importance of stain normalization in colorectal tissue classification with convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology")
  Member "classification and retrieval of digital pathology scans a new dataset":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning with less data via weakly labeled patch classification in digital pathology")
  Member "a survey on deep learning in medical image analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "medgan medical image translation using gans")
  Member "neural stain style transfer learning using gan for histopathological images":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep learning models for digital pathology")
  Member "constrained deep weak supervision for histopathology image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "non parametric uni modality constraints for deep ordinal classification")
  Member "visualizing deep neural network decisions prediction difference analysis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning")

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)