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: "fairwashing the risk of rationalization"
  Abstract: Black-box explanation is the problem of explaining how a machine learning model -- whose internal logic is hidden to the auditor and generally complex -- produces its outcomes. Current approaches for solving this problem include model explanation, outcome explanation as well as model inspection. While these techniques can be beneficial by providing interpretability, they can be used in a negative manner to perform fairwashing, which we define as promoting the false perception that a machine learning model respects some ethical values. In particular, we demonstrate that it is possible to systematically rationalize decisions taken by an unfair black-box model using the model explanation as well as the outcome explanation approaches with a given fairness metric. Our solution, LaundryML, is based on a regularized rule list enumeration algorithm whose objective is to search for fair rule lists approximating an unfair black-box model. We empirically evaluate our rationalization technique on black-box models trained on real-world datasets and show that one can obtain rule lists with high fidelity to the black-box model while being considerably less unfair at the same time.

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"
    - "fooling neural network interpretations via adversarial model manipulation"
    - "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"
    - "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"
    - "how do i fool you manipulating user trust via misleading black box explanations"

Connected through hyperedge HE-3 (induced by source paper "predictive multiplicity in classification"):
  with members:
    - "a study in rashomon curves and volumes a new perspective on generalization and model simplicity in machine learning"
    - "problem formulation and fairness"
    - "aequitas a bias and fairness audit toolkit"
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"
    - "classification with fairness constraints a meta algorithm with provable guarantees"
    - "how to use heuristics for differential privacy"
    - "the dataset nutrition label a framework to drive higher data quality standards"
    - "interventions over predictions reframing the ethical debate for actuarial risk assessment"

Connected through hyperedge HE-4 (induced by source paper "model agnostic linear competitors when interpretable models compete and collaborate with black box models"):
  with members:
    - "i know you ll be back interpretable new user clustering and churn prediction on a mobile social application"
    - "the dangers of post hoc interpretability unjustified counterfactual explanations"
    - "an interpretable model with globally consistent explanations for credit risk"
    - "on the robustness of interpretability methods"
    - "interpretable explorable approximations of black box models"
    - "a unified approach to interpreting model predictions"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "harnessing deep neural networks with logic rules"

Connected through hyperedge HE-5 (induced by source paper "the bouncer problem challenges to remote explainability"):
  with members:
    - "deep learning recommendation model for personalization and recommendation systems"
    - "representer point selection for explaining deep neural networks"
    - "explainable recommendation a survey and new perspectives"
    - "explanation in artificial intelligence insights from the social sciences"
    - "fairness testing testing software for discrimination"
    - "fairness in machine learning lessons from political philosophy"
    - "logics and practices of transparency and opacity in real world applications of public sector machine learning"
    - "stealing machine learning models via prediction apis"

Connected through hyperedge HE-6 (induced by source paper "fooling lime and shap adversarial attacks on post hoc explanation methods"):
  with members:
    - "fooling neural network interpretations via adversarial model manipulation"
    - "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"

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)