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 "chexclusion fairness gaps in deep chest x ray classifiers"):
  Abstract: Machine learning systems have received much attention recently for their ability to achieve expert-level performance on clinical tasks, particularly in medical imaging. Here, we examine the extent to which state-of-the-art deep learning classifiers trained to yield diagnostic labels from X-ray images are biased with respect to protected attributes. We train convolution neural networks to predict 14 diagnostic labels in three prominent public chest X-ray datasets: MIMIC-CXR, Chest-Xray8, and CheXpert. We then evaluate the TPR disparity - the difference in true positive rates (TPR) and - underdiagnosis rate - the false positive rate of a non-diagnosis - among different protected attributes such as patient sex, age, race, and insurance type. We demonstrate that TPR disparities exist in the state-of-the-art classifiers in all datasets, for all clinical tasks, and all subgroups. We find that TPR disparities are most commonly not significantly correlated with a subgroup's proportional disease burden; further, we find that some subgroups and subsection of the population are chronically underdiagnosed. Such performance disparities have real consequences as models move from papers to products, and should be carefully audited prior to deployment.

Includes members:
  Member "bias in bios a case study of semantic representation bias in a high stakes setting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an attentive survey of attention models")
  Member "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "algorithmic fairness")
  Member "a simulation based dynamic evaluation framework for system wide algorithmic fairness":
    also connected through other hyperedges:
      (none)
  Member "on the fairness of disentangled representations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "nestedvae isolating common factors via weak supervision")
  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 "an adversarial approach for the robust classification of pneumonia from chest radiographs")
  Member "learning to diagnose from scratch by exploiting dependencies among labels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dynamic graph correlation learning for disease diagnosis with incomplete labels")
  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-6 (induced by source paper "right for the wrong scientific reasons revising deep networks by interacting with their explanations")
  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")

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)