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 "an adversarial approach for the robust classification of pneumonia from chest radiographs"):
  Abstract: While deep learning has shown promise in the domain of disease classification from medical images, models based on state-of-the-art convolutional neural network architectures often exhibit performance loss due to dataset shift. Models trained using data from one hospital system achieve high predictive performance when tested on data from the same hospital, but perform significantly worse when they are tested in different hospital systems. Furthermore, even within a given hospital system, deep learning models have been shown to depend on hospital- and patient-level confounders rather than meaningful pathology to make classifications. In order for these models to be safely deployed, we would like to ensure that they do not use confounding variables to make their classification, and that they will work well even when tested on images from hospitals that were not included in the training data. We attempt to address this problem in the context of pneumonia classification from chest radiographs. We propose an approach based on adversarial optimization, which allows us to learn more robust models that do not depend on confounders. Specifically, we demonstrate improved out-of-hospital generalization performance of a pneumonia classifier by training a model that is invariant to the view position of chest radiographs (anterior-posterior vs. posterior-anterior). Our approach leads to better predictive performance on external hospital data than both a standard baseline and previously proposed methods to handle confounding, and also suggests a method for identifying models that may rely on confounders. Code available at this https URL.

Includes members:
  Member "grad cam visual explanations from deep networks via gradient based localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "learning explainable models using attribution priors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "explaining explanations axiomatic feature interactions for deep networks")
  Member "chexpert a large chest radiograph dataset with uncertainty labels and expert comparison":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "continual learning for domain adaptation in chest x ray classification")
  Member "mimic cxr jpg a large publicly available database of labeled chest radiographs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "chexclusion fairness gaps in deep chest x ray classifiers")
  Member "transfusion understanding transfer learning for medical imaging":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving the adversarial robustness of transfer learning via noisy feature distillation")
  Member "achieving fairness through adversarial learning an application to recidivism prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "algorithmic fairness")
  Member "deep learning predicts hip fracture using confounding patient and healthcare variables":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "right for the wrong scientific reasons revising deep networks by interacting with their explanations")
  Member "variable generalization performance of a deep learning model to detect pneumonia in chest radiographs a cross sectional study":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "right for the wrong scientific reasons revising deep networks by interacting with their explanations")

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)