human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "gradients of counterfactuals"
  Abstract: Gradients have been used to quantify feature importance in machine learning models. Unfortunately, in nonlinear deep networks, not only individual neurons but also the whole network can saturate, and as a result an important input feature can have a tiny gradient. We study various networks, and observe that this phenomena is indeed widespread, across many inputs.#N##N#We propose to examine interior gradients, which are gradients of counterfactual inputs constructed by scaling down the original input. We apply our method to the GoogleNet architecture for object recognition in images, as well as a ligand-based virtual screening network with categorical features and an LSTM based language model for the Penn Treebank dataset. We visualize how interior gradients better capture feature importance. Furthermore, interior gradients are applicable to a wide variety of deep networks, and have the attribution property that the feature importance scores sum to the the prediction score. #N##N#Best of all, interior gradients can be computed just as easily as gradients. In contrast, previous methods are complex to implement, which hinders practical adoption.

Connected through hyperedge HE-1 (induced by source paper "robust saliency maps with decoy enhanced saliency score"):
  with members:
    - "understanding impacts of high order loss approximations and features in deep learning interpretation"
    - "certifiably robust interpretation in deep learning"
    - "interpreting neural networks using flip points"
    - "counterfactual visual explanations"
    - "explaining image classifiers by counterfactual generation"
    - "sanity checks for saliency maps"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "deeppink reproducible feature selection in deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "a point wise linear model reveals reasons for 30 day readmission of heart failure patients"):
  with members:
    - "scalable and accurate deep learning for electronic health records"
    - "self normalizing neural networks"
    - "learning important features through propagating activation differences"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "why should i trust you explaining the predictions of any classifier"
    - "visualizing and understanding convolutional networks"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-3 (induced by source paper "why are saliency maps noisy cause of and solution to noisy saliency maps"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "bridging adversarial robustness and gradient interpretability"
    - "theoretically principled trade off between robustness and accuracy"
    - "a theoretical explanation for perplexing behaviors of backpropagation based visualizations"
    - "evaluating feature importance estimates"
    - "smoothgrad removing noise by adding noise"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-4 (induced by source paper "evaluating explainers via perturbation"):
  with members:
    - "smoothgrad removing noise by adding noise"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-5 (induced by source paper "explaining deep learning models a bayesian non parametric approach"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning important features through propagating activation differences"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "distilling a neural network into a soft decision tree"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-6 (induced by source paper "training set debugging using trusted items"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "smoothgrad removing noise by adding noise"
    - "algorithmic decision making and the cost of fairness"
    - "equality of opportunity in supervised learning"
    - "identifying mislabeled training data"

Connected through hyperedge HE-7 (induced by source paper "rank projection trees for multilevel neural network interpretation"):
  with members:
    - "learning important features through propagating activation differences"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "not just a black box learning important features through propagating activation differences"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-8 (induced by source paper "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"):
  with members:
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "smoothgrad removing noise by adding noise"
    - "improved training of wasserstein gans"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "synthesizing robust adversarial examples"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "practical black box attacks against machine learning"
    - "why should i trust you explaining the predictions of any classifier"

Question: Please tell me which class the center node 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)