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: "how do i fool you manipulating user trust via misleading black box explanations"
  Abstract: As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice, there has been a growing emphasis on developing techniques for explaining these black boxes in a human interpretable manner. It has recently become apparent that a high-fidelity explanation of a black box ML model may not accurately reflect the biases in the black box. As a consequence, explanations have the potential to mislead human users into trusting a problematic black box. In this work, we rigorously explore the notion of misleading explanations and how they influence user trust in black-box models. More specifically, we propose a novel theoretical framework for understanding and generating misleading explanations, and carry out a user study with domain experts to demonstrate how these explanations can be used to mislead users. Our work is the first to empirically establish how user trust in black box models can be manipulated via misleading explanations.

Connected through hyperedge HE-1 (induced by source paper "auditing and debugging deep learning models via decision boundaries individual level and group level analysis"):
  with members:
    - "an evaluation of the human interpretability of explanation"
    - "fooling lime and shap adversarial attacks on post hoc explanation methods"
    - "measurable counterfactual local explanations for any classifier"
    - "how can we fool lime and shap adversarial attacks on post hoc explanation methods"
    - "on the accuracy of influence functions for measuring group effects"
    - "refining the structure of neural networks using matrix conditioning"
    - "interpreting neural networks using flip points"
    - "efficient search for diverse coherent explanations"

Connected through hyperedge HE-2 (induced by source paper "deceptive ai explanations creation and detection"):
  with members:
    - "human grounded evaluations of explanation methods for text classification"
    - "a survey on bias and fairness in machine learning"
    - "how to manipulate cnns to make them lie the gradcam case"
    - "generating sentiment preserving fake online reviews using neural language models and their human and machine based detection"
    - "how model accuracy and explanation fidelity influence user trust"
    - "perturbation sensitivity analysis to detect unintended model biases"
    - "rise randomized input sampling for explanation of black box models"
    - "sanity checks for saliency maps"

Connected through hyperedge HE-3 (induced by source paper "proxy tasks and subjective measures can be misleading in evaluating explainable ai systems"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "on human predictions with explanations and predictions of machine learning models a case study on deception detection"
    - "human centered tools for coping with imperfect algorithms during medical decision making"
    - "metrics for explainable ai challenges and prospects"
    - "explaining explanations an overview of interpretability of machine learning"
    - "manipulating and measuring model interpretability"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-4 (induced by source paper "a case for humans in the loop decisions in the presence of erroneous algorithmic scores"):
  with members:
    - "learning representations by humans for humans"
    - "investigating human machine complementarity for recidivism predictions"
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"

Connected through hyperedge HE-5 (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"
    - "fooling neural network interpretations via adversarial model manipulation"

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.AI(Artificial Intelligence)