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 "robust saliency maps with decoy enhanced saliency score"):
  Abstract: Saliency methods help to make deep neural network predictions more interpretable by identifying particular features, such as pixels in an image, that contribute most strongly to the network's prediction. Unfortunately, recent evidence suggests that many saliency methods perform poorly when gradients are saturated or in the presence of strong inter-feature dependence or noise injected by an adversarial attack. In this work, we propose to infer robust saliency scores by integrating the saliency scores of a set of decoys with a novel decoy-enhanced saliency score, in which the decoys are generated by either solving an optimization problem or blurring the original input. We theoretically analyze that our method compensates for gradient saturation and considers joint activation patterns of pixels. We also apply our method to three different CNNs---VGGNet, AlexNet, and ResNet trained on ImageNet data set. The empirical results show both qualitatively and quantitatively that our method outperforms raw scores produced by three existing saliency methods, even in the presence of adversarial attacks.

Includes members:
  Member "understanding impacts of high order loss approximations and features in deep learning interpretation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "explainable machine learning in deployment")
  Member "certifiably robust interpretation in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "de randomized smoothing for certifiable defense against patch attacks")
  Member "interpreting neural networks using flip points":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "auditing and debugging deep learning models via decision boundaries individual level and group level analysis")
  Member "counterfactual visual explanations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "explaining image classifiers by counterfactual generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sam the sensitivity of attribution methods to hyperparameters")
  Member "sanity checks for saliency maps":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "learning to explain an information theoretic perspective on model interpretation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "importance driven deep learning system testing")
  Member "deeppink reproducible feature selection in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fsnet feature selection network on high dimensional biological data")

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)