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: "a theoretical explanation for perplexing behaviors of backpropagation based visualizations"
  Abstract: Backpropagation-based visualizations have been proposed to interpret convolutional neural networks (CNNs), however a theory is missing to justify their behaviors: Guided backpropagation (GBP) and deconvolutional network (DeconvNet) generate more human-interpretable but less class-sensitive visualizations than saliency map. Motivated by this, we develop a theoretical explanation revealing that GBP and DeconvNet are essentially doing (partial) image recovery which is unrelated to the network decisions. Specifically, our analysis shows that the backward ReLU introduced by GBP and DeconvNet, and the local connections in CNNs are the two main causes of compelling visualizations. Extensive experiments are provided that support the theoretical analysis.

Connected through hyperedge HE-1 (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"
    - "understanding individual decisions of cnns via contrastive backpropagation"
    - "sanity checks for saliency maps"

Connected through hyperedge HE-2 (induced by source paper "what do adversarially robust models look at"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "scaling provable adversarial defenses"
    - "a dual approach to scalable verification of deep networks"
    - "deep clustering for unsupervised learning of visual features"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "robustness via curvature regularization and vice versa"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "training for faster adversarial robustness verification via inducing relu stability"

Connected through hyperedge HE-3 (induced by source paper "interpretable and fine grained visual explanations for convolutional neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "towards explanation of dnn based prediction with guided feature inversion"
    - "visual interpretability for deep learning a survey"
    - "regional multi scale approach for visually pleasing explanations of deep neural networks"
    - "techniques for interpretable machine learning"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "explaining image classifiers by counterfactual generation"
    - "rise randomized input sampling for explanation of black box models"

Connected through hyperedge HE-4 (induced by source paper "interpretations are useful penalizing explanations to align neural networks with prior knowledge"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "aggregating explainability methods for neural networks stabilizes explanations"
    - "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
    - "embedding human knowledge in deep neural network via attention map"
    - "repair removing representation bias by dataset resampling"
    - "incorporating priors with feature attribution on text classification"
    - "learning explainable models using attribution priors"
    - "attention is not explanation"

Connected through hyperedge HE-5 (induced by source paper "improving feature attribution through input specific network pruning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "full gradient representation for neural network visualization"
    - "explaining deep neural networks with a polynomial time algorithm for shapley values approximation"
    - "understanding deep networks via extremal perturbations and smooth masks"
    - "interpretable and fine grained visual explanations for convolutional neural networks"
    - "a benchmark for interpretability methods in deep neural networks"
    - "visualizing deep networks by optimizing with integrated gradients"
    - "why are saliency maps noisy cause of and solution to noisy saliency maps"

Connected through hyperedge HE-6 (induced by source paper "bridging adversarial robustness and gradient interpretability"):
  with members:
    - "theoretically principled trade off between robustness and accuracy"
    - "disentangling adversarial robustness and generalization"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "spectral normalization for generative adversarial networks"
    - "adversarial learning and explainability in structured datasets"
    - "is robustness the cost of accuracy a comprehensive study on the robustness of 18 deep image classification models"
    - "concise and stable explanations using adversarial training"
    - "sanity checks for saliency maps"

Connected through hyperedge HE-7 (induced by source paper "semantics for global and local interpretation of deep neural networks"):
  with members:
    - "sanity checks for saliency maps"
    - "evaluating feature importance estimates"
    - "on the robustness of interpretability methods"
    - "smoothgrad removing noise by adding noise"
    - "network dissection quantifying interpretability of deep visual representations"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"
    - "towards better understanding of gradient based attribution methods for deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "full gradient representation for neural network visualization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "knowledge transfer with jacobian matching"
    - "knowledge transfer via distillation of activation boundaries formed by hidden neurons"
    - "sanity checks for saliency maps"
    - "evaluating feature importance estimates"
    - "smoothgrad removing noise by adding noise"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "axiomatic attribution for deep networks"

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)