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: "on human predictions with explanations and predictions of machine learning models a case study on deception detection"
  Abstract: Humans are the final decision makers in critical tasks that involve ethical and legal concerns, ranging from recidivism prediction, to medical diagnosis, to fighting against fake news. Although machine learning models can sometimes achieve impressive performance in these tasks, these tasks are not amenable to full automation. To realize the potential of machine learning for improving human decisions, it is important to understand how assistance from machine learning models affects human performance and human agency.   In this paper, we use deception detection as a testbed and investigate how we can harness explanations and predictions of machine learning models to improve human performance while retaining human agency. We propose a spectrum between full human agency and full automation, and develop varying levels of machine assistance along the spectrum that gradually increase the influence of machine predictions. We find that without showing predicted labels, explanations alone slightly improve human performance in the end task. In comparison, human performance is greatly improved by showing predicted labels (>20% relative improvement) and can be further improved by explicitly suggesting strong machine performance. Interestingly, when predicted labels are shown, explanations of machine predictions induce a similar level of accuracy as an explicit statement of strong machine performance. Our results demonstrate a tradeoff between human performance and human agency and show that explanations of machine predictions can moderate this tradeoff.

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"
    - "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"
    - "a survey of evaluation methods and measures for interpretable machine learning"

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"
    - "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"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "evaluating saliency map explanations for convolutional neural networks a user study"):
  with members:
    - "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"
    - "manipulating and measuring model interpretability"
    - "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 "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"
    - "stopping active learning based on predicted change of f measure for text classification"
    - "explaining models an empirical study of how explanations impact fairness judgment"
    - "manipulating and measuring model interpretability"
    - "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-5 (induced by source paper "effect of confidence and explanation on accuracy and trust calibration in ai assisted decision making"):
  with members:
    - "explaining models an empirical study of how explanations impact fairness judgment"
    - "metrics for explainable ai challenges and prospects"
    - "manipulating and measuring model interpretability"
    - "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"

Connected through hyperedge HE-6 (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"
    - "how do i fool you manipulating user trust via misleading black box explanations"
    - "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-7 (induced by source paper "harnessing explanations to bridge ai and humans"):
  with members:
    - "why is chicago deceptive towards building model driven tutorials for humans"
    - "an evaluation of the human interpretability of explanation"
    - "learning the difference that makes a difference with counterfactually augmented data"
    - "manipulating and measuring model interpretability"
    - "rationalizing neural predictions"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-8 (induced by source paper "visual interaction with deep learning models through collaborative semantic inference"):
  with members:
    - "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"
    - "semantic image synthesis with spatially adaptive normalization"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "a tutorial on deep latent variable models of natural language"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "bottom up abstractive summarization"
    - "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"
    - "gan dissection visualizing and understanding generative adversarial 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.AI(Artificial Intelligence)