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 for whole slide image analysis an overview"):
  Abstract: The widespread adoption of whole slide imaging has increased the demand for effective and efficient gigapixel image analysis. Deep learning is at the forefront of computer vision, showcasing significant improvements over previous methodologies on visual understanding. However, whole slide images have billions of pixels and suffer from high morphological heterogeneity as well as from different types of artefacts. Collectively, these impede the conventional use of deep learning. For the clinical translation of deep learning solutions to become a reality, these challenges need to be addressed. In this paper, we review work on the interdisciplinary attempt of training deep neural networks using whole slide images, and highlight the different ideas underlying these methodologies.

Includes members:
  Member "learning where to see a novel attention model for automated immunohistochemical scoring":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features")
  Member "predicting breast tumor proliferation from whole slide images the tupac16 challenge":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "streaming convolutional neural networks for end to end learning with multi megapixel images")
  Member "neural image compression for gigapixel histopathology image analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "streaming convolutional neural networks for end to end learning with multi megapixel images")
  Member "her2 challenge contest a detailed assessment of automated her2 scoring algorithms in whole slide images of breast cancer tissues":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "active and incremental learning with weak supervision")
  Member "attention based deep multiple instance learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction")
  Member "classification and disease localization in histopathology using only global labels a weakly supervised approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weakly supervised medical diagnosis and localization from multiple resolutions")
  Member "cancer metastasis detection with neural conditional random field":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "building footprint generation by integratingconvolution neural network with feature pairwiseconditional random field fpcrf")
  Member "terabyte scale deep multiple instance learning for classification and localization in pathology":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an attention based multi resolution model for prostate whole slide imageclassification and localization")

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)