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: "manipulating and measuring model interpretability"
  Abstract: Despite a growing body of research focused on creating interpretable machine learning methods, there have been few empirical studies verifying whether interpretable methods achieve their intended effects on end users. We present a framework for assessing the effects of model interpretability on users via pre-registered experiments in which participants are shown functionally identical models that vary in factors thought to influence interpretability. Using this framework, we ran a sequence of large-scale randomized experiments, varying two putative drivers of interpretability: the number of features and the model transparency (clear or black-box). We measured how these factors impact trust in model predictions, the ability to simulate a model, and the ability to detect a model's mistakes. We found that participants who were shown a clear model with a small number of features were better able to simulate the model's predictions. However, we found no difference in multiple measures of trust and found that clear models did not improve the ability to correct mistakes. These findings suggest that interpretability research could benefit from more emphasis on empirically verifying that interpretable models achieve all their intended effects.

Connected through hyperedge HE-1 (induced by source paper "questioning the ai informing design practices for explainable ai user experiences"):
  with members:
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "a grounded interaction protocol for explainable artificial intelligence"
    - "personalized explanation in machine learning"
    - "on human predictions with explanations and predictions of machine learning models a case study on deception detection"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "explaining models an empirical study of how explanations impact fairness judgment"
    - "the challenge of crafting intelligible intelligence"
    - "metrics for explainable ai challenges and prospects"

Connected through hyperedge HE-2 (induced by source paper "why is chicago deceptive towards building model driven tutorials for humans"):
  with members:
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "on human predictions with explanations and predictions of machine learning models a case study on deception detection"
    - "a human grounded evaluation of shap for alert processing"
    - "rating reliability and bias in news articles does ai assistance help everyone"
    - "human centered tools for coping with imperfect algorithms during medical decision making"
    - "efficient search for diverse coherent explanations"
    - "local rule based explanations of black box decision systems"
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"

Connected through hyperedge HE-3 (induced by source paper "evaluating saliency map explanations for convolutional neural networks a user study"):
  with members:
    - "on human predictions with explanations and predictions of machine learning models a case study on deception detection"
    - "minimalistic explanations capturing the essence of decisions"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "what can ai do for me evaluating machine learning interpretations in cooperative play"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "explanation in artificial intelligence insights from the social sciences"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "generating visual explanations"

Connected through hyperedge HE-4 (induced by source paper "factors influencing perceived fairness in algorithmic decision making algorithm outcomes development procedures and individual differences"):
  with members:
    - "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "society in the loop programming the algorithmic social contract"
    - "the frontiers of fairness in machine learning"
    - "fairness and accountability design needs for algorithmic support in high stakes public sector decision making"
    - "a reductions approach to fair classification"
    - "demographics and discussion influence views on algorithmic fairness"
    - "network dissection quantifying interpretability of deep visual representations"

Connected through hyperedge HE-5 (induced by source paper "problems with shapley value based explanations as feature importance measures"):
  with members:
    - "the hidden assumptions behind counterfactual explanations and principal reasons"
    - "fooling lime and shap adversarial attacks on post hoc explanation methods"
    - "explainable machine learning in deployment"
    - "the explanation game explaining machine learning models with cooperative game theory"
    - "consistent individualized feature attribution for tree ensembles"
    - "trust in data science collaboration translation and accountability in corporate data science projects"
    - "explanation in artificial intelligence insights from the social sciences"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"

Connected through hyperedge HE-6 (induced by source paper "vice visual counterfactual explanations for machine learning models"):
  with members:
    - "the what if tool interactive probing of machine learning models"
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "manifold a model agnostic framework for interpretation and diagnosis of machine learning models"
    - "rulematrix visualizing and understanding classifiers with rules"
    - "a survey of methods for explaining black box models"
    - "explanation in artificial intelligence insights from the social sciences"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-7 (induced by source paper "explainable active learning xal an empirical study of how local explanations impact annotator experience"):
  with members:
    - "one explanation does not fit all a toolkit and taxonomy of ai explainability techniques"
    - "on human predictions with explanations and predictions of machine learning models a case study on deception detection"
    - "stopping active learning based on predicted change of f measure for text classification"
    - "explaining models an empirical study of how explanations impact fairness judgment"
    - "impact of batch size on stopping active learning for text classification"
    - "a unified approach to interpreting model predictions"
    - "machine teaching a new paradigm for building machine learning systems"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-8 (induced by source paper "effect of confidence and explanation on accuracy and trust calibration in ai assisted decision making"):
  with members:
    - "on human predictions with explanations and predictions of machine learning models a case study on deception detection"
    - "explaining models an empirical study of how explanations impact fairness judgment"
    - "metrics for explainable ai challenges and prospects"
    - "explanation in artificial intelligence insights from the social sciences"
    - "a unified approach to interpreting model predictions"
    - "why should i trust you explaining the predictions of any classifier"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"

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)