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: "understanding individual decisions of cnns via contrastive backpropagation"
  Abstract: A number of backpropagation-based approaches such as DeConvNets, vanilla Gradient Visualization and Guided Backpropagation have been proposed to better understand individual decisions of deep convolutional neural networks. The saliency maps produced by them are proven to be non-discriminative. Recently, the Layer-wise Relevance Propagation (LRP) approach was proposed to explain the classification decisions of rectifier neural networks. In this work, we evaluate the discriminativeness of the generated explanations and analyze the theoretical foundation of LRP, i.e. Deep Taylor Decomposition. The experiments and analysis conclude that the explanations generated by LRP are not class-discriminative. Based on LRP, we propose Contrastive Layer-wise Relevance Propagation (CLRP), which is capable of producing instance-specific, class-discriminative, pixel-wise explanations. In the experiments, we use the CLRP to explain the decisions and understand the difference between neurons in individual classification decisions. We also evaluate the explanations quantitatively with a Pointing Game and an ablation study. Both qualitative and quantitative evaluations show that the CLRP generates better explanations than the LRP.

Connected through hyperedge HE-1 (induced by source paper "universal adversarial attack on attention and the resulting dataset damagenet"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "one pixel attack for fooling deep neural networks"
    - "image super resolution as a defense against adversarial attacks"
    - "explaining convolutional neural networks using softmax gradient layer wise relevance propagation"
    - "yet another but more efficient black box adversarial attack tiling and evolution strategies"
    - "natural adversarial examples"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "interpreting adversarially trained convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "search for better students to learn distilled knowledge"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "exploring randomly wired neural networks for image recognition"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "knowledge distillation with adversarial samples supporting decision boundary"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "darts differentiable architecture search"
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "rethinking the value of network pruning"

Connected through hyperedge HE-3 (induced by source paper "when explanations lie why many modified bp attributions fail"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "fooling lime and shap adversarial attacks on post hoc explanation methods"
    - "how to manipulate cnns to make them lie the gradcam case"
    - "why are saliency maps noisy cause of and solution to noisy saliency maps"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "innvestigate neural networks"
    - "a theoretical explanation for perplexing behaviors of backpropagation based visualizations"
    - "sanity checks for saliency maps"

Connected through hyperedge HE-4 (induced by source paper "explaining convolutional neural networks using softmax gradient layer wise relevance propagation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "visualizing deep networks by optimizing with integrated gradients"
    - "deep features analysis with attention networks"
    - "how do convolutional neural networks learn design"
    - "innvestigate neural networks"
    - "attention branch network learning of attention mechanism for visual explanation"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-5 (induced by source paper "contextual prediction difference analysis"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "delayed impact of fair machine learning"
    - "textual explanations for self driving vehicles"
    - "sanity checks for saliency maps"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "smoothgrad removing noise by adding noise"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"

Connected through hyperedge HE-6 (induced by source paper "saliency methods for explaining adversarial attacks"):
  with members:
    - "a theoretical explanation for perplexing behaviors of backpropagation based visualizations"
    - "sanity checks for saliency maps"
    - "on the robustness of interpretability methods"
    - "smoothgrad removing noise by adding noise"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "interpretable explanations of black boxes by meaningful perturbation"

Connected through hyperedge HE-7 (induced by source paper "towards best practice in explaining neural network decisions with lrp"):
  with members:
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "analyzing neuroimaging data through recurrent deep learning models"
    - "innvestigate neural networks"
    - "smoothgrad removing noise by adding noise"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "axiomatic attribution for deep networks"
    - "the shattered gradients problem if resnets are the answer then what is the question"
    - "controlling explanatory heatmap resolution and semantics via decomposition depth"

Connected through hyperedge HE-8 (induced by source paper "understanding bias in machine learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "smoothgrad removing noise by adding noise"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "why should i trust you explaining the predictions of any classifier"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"

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.CV(Computer Vision and Pattern Recognition)