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 semi supervised generative learning for automated tumor proportion scoring on nsclc tissue needle biopsies"):
  Abstract: The level of PD-L1 expression in immunohistochemistry (IHC) assays is a key biomarker for the identification of Non-Small-Cell-Lung-Cancer (NSCLC) patients that may respond to anti PD-1/PD-L1 treatments. The quantification of PD-L1 expression currently includes the visual estimation by a pathologist of the percentage (tumor proportional scoring or TPS) of tumor cells showing PD-L1 staining. Known challenges like differences in positivity estimation around clinically relevant cut-offs and sub-optimal quality of samples makes visual scoring tedious and subjective, yielding a scoring variability between pathologists. In this work, we propose a novel deep learning solution that enables the first automated and objective scoring of PD-L1 expression in late stage NSCLC needle biopsies. To account for the low amount of tissue available in biopsy images and to restrict the amount of manual annotations necessary for training, we explore the use of semi-supervised approaches against standard fully supervised methods. We consolidate the manual annotations used for training as well the visual TPS scores used for quantitative evaluation with multiple pathologists. Concordance measures computed on a set of slides unseen during training provide evidence that our automatic scoring method matches visual scoring on the considered dataset while ensuring repeatability and objectivity.

Includes members:
  Member "staingan stain style transfer for digital histological images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "style transfer based image synthesis as an efficient regularization technique in deep learning")
  Member "spectral normalization for generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "machine learning methods for histopathological image analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi stream faster rcnn for mitosis counting in breast cancer images")
  Member "terabyte scale deep multiple instance learning for classification and localization in pathology":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an attention based multi resolution model for prostate whole slide imageclassification and localization")
  Member "convolutional neural networks for histopathology image classification training vs using pre trained networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving prostate cancer detection with breast histopathology images")
  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 "augmenting supervised neural networks with unsupervised objectives for large scale image classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "image embedded segmentation combining supervised and unsupervised objectives through generative adversarial networks")
  Member "infogan interpretable representation learning by information maximizing generative adversarial nets":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")

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)