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 "justifying diagnosis decisions by deep neural networks"):
  Abstract: Abstract   An integrated approach is proposed across visual and textual data to both determine and justify a medical diagnosis by a neural network. As deep learning techniques improve, interest grows to apply them in medical applications. To enable a transition to workflows in a medical context that are aided by machine learning, the need exists for such algorithms to help justify the obtained outcome so human clinicians can judge their validity. In this work, deep learning methods are used to map a frontal X-ray image to a continuous textual representation. This textual representation is decoded into a diagnosis and the associated textual justification that will help a clinician evaluate the outcome. Additionally, more explanatory data is provided for the diagnosis by generating a realistic X-ray that belongs to the nearest alternative diagnosis. We demonstrate in a clinical expert opinion study on a subset of the X-ray data set from the Indiana University hospital network that our justification mechanism significantly outperforms existing methods that use saliency maps. While performing multi-task training with multiple loss functions, our method achieves excellent diagnosis accuracy and captioning quality when compared to current state-of-the-art single-task methods.

Includes members:
  Member "on the automatic generation of medical imaging reports":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "defense gan protecting classifiers against adversarial attacks using generative models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "tienet text image embedding network for common thorax disease classification and reporting in chest x rays":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "quantitatively evaluating gans with divergences proposed for training":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "object centric image generation from layouts")
  Member "generating continuous representations of medical texts":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "latent code and text based generative adversarial networks for soft text generation")
  Member "tandemnet distilling knowledge from medical images using diagnostic reports as optional semantic references":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on biomedical image captioning")
  Member "chexnet radiologist level pneumonia detection on chest x rays with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an interpretable classifier for high resolution breast cancer screening images utilizing weakly supervised localization")
  Member "progressive growing of gans for improved quality stability and variation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

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.LG(Machine Learning)