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: "fooling neural network interpretations via adversarial model manipulation"
  Abstract: We ask whether the neural network interpretation methods can be fooled via adversarial model manipulation, which is defined as a model fine-tuning step that aims to radically alter the explanations without hurting the accuracy of the original model. By incorporating the interpretation results directly in the regularization term of the objective function for fine-tuning, we show that the state-of-the-art interpreters, e.g., LRP and Grad-CAM, can be easily fooled with our model manipulation. We propose two types of fooling, passive and active, and demonstrate such foolings generalize well to the entire validation set as well as transfer to other interpretation methods. Our results are validated by both visually showing the fooled explanations and reporting quantitative metrics that measure the deviations from the original explanations. We claim that the stability of neural network interpretation method with respect to our adversarial model manipulation is an important criterion to check for developing robust and reliable neural network interpretation method.

Connected through hyperedge HE-1 (induced by source paper "aggregating explanation methods for stable and robust explainability"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "fairwashing the risk of rationalization"
    - "interpretable deep learning under fire"
    - "a human grounded evaluation benchmark for local explanations of machine learning"
    - "explaining image classifiers by counterfactual generation"
    - "sanity checks for saliency maps"
    - "smoothgrad removing noise by adding noise"
    - "explaining nonlinear classification decisions with deep taylor decomposition"

Connected through hyperedge HE-2 (induced by source paper "learning fair rule lists"):
  with members:
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "the bouncer problem challenges to remote explainability"
    - "fairwashing the risk of rationalization"
    - "the dangers of post hoc interpretability unjustified counterfactual explanations"
    - "learning optimal and fair decision trees for non discriminative decision making"
    - "faht an adaptive fairness aware decision tree classifier"
    - "how can we fool lime and shap adversarial attacks on post hoc explanation methods"
    - "how do i fool you manipulating user trust via misleading black box explanations"

Connected through hyperedge HE-3 (induced by source paper "explaining deep learning based networked systems"):
  with members:
    - "neural packet classification"
    - "hyper sagnn a self attention based graph neural network for hypergraphs"
    - "tensorflow js machine learning for the web and beyond"
    - "pforest in network inference with random forests"
    - "interpreting and improving natural language processing in machines with natural language processing in the brain"
    - "gnn explainer a tool for post hoc explanation of graph neural networks"
    - "mmalfm explainable recommendation by leveraging reviews and images"
    - "unveiling the potential of graph neural networks for network modeling and optimization in sdn"

Connected through hyperedge HE-4 (induced by source paper "fooling network interpretation in image classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "one pixel attack for fooling deep neural networks"
    - "lavan localized and visible adversarial noise"
    - "sanity checks for saliency maps"
    - "tell me where to look guided attention inference network"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "darts deceiving autonomous cars with toxic signs"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-5 (induced by source paper "benchmarking attribution methods with relative feature importance"):
  with members:
    - "a theoretical explanation for perplexing behaviors of backpropagation based visualizations"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "excessive invariance causes adversarial vulnerability"
    - "manipulating and measuring model interpretability"
    - "sanity checks for saliency maps"
    - "evaluating feature importance estimates"
    - "smoothgrad removing noise by adding noise"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-6 (induced by source paper "bim towards quantitative evaluation of interpretability methods with ground truth"):
  with members:
    - "a theoretical explanation for perplexing behaviors of backpropagation based visualizations"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "excessive invariance causes adversarial vulnerability"
    - "manipulating and measuring model interpretability"
    - "sanity checks for saliency maps"
    - "evaluating feature importance estimates"
    - "smoothgrad removing noise by adding noise"
    - "towards better understanding of gradient based attribution methods for deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "fooling lime and shap adversarial attacks on post hoc explanation methods"):
  with members:
    - "fairwashing the risk of rationalization"
    - "explaining explanations in ai"
    - "global explanations of neural networks mapping the landscape of predictions"
    - "a unified approach to interpreting model predictions"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-8 (induced by source paper "towards hiding adversarial examples from network interpretation"):
  with members:
    - "adversarial patch"
    - "adversarial framing for image and video classification"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "lavan localized and visible adversarial noise"
    - "sanity checks for saliency maps"
    - "tell me where to look guided attention inference network"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "darts deceiving autonomous cars with toxic signs"

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)