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: "metrics for explainable ai challenges and prospects"
  Abstract: The question addressed in this paper is: If we present to a user an AI system that explains how it works, how do we know whether the explanation works and the user has achieved a pragmatic understanding of the AI? In other words, how do we know that an explanainable AI system (XAI) is any good? Our focus is on the key concepts of measurement. We discuss specific methods for evaluating: (1) the goodness of explanations, (2) whether users are satisfied by explanations, (3) how well users understand the AI systems, (4) how curiosity motivates the search for explanations, (5) whether the user's trust and reliance on the AI are appropriate, and finally, (6) how the human-XAI work system performs. The recommendations we present derive from our integration of extensive research literatures and our own psychometric evaluations.

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

Connected through hyperedge HE-2 (induced by source paper "trust in automl exploring information needs for establishing trust in automated machine learning systems"):
  with members:
    - "effect of confidence and explanation on accuracy and trust calibration in ai assisted decision making"
    - "questioning the ai informing design practices for explainable ai user experiences"
    - "one explanation does not fit all a toolkit and taxonomy of ai explainability techniques"
    - "an admm based framework for automl pipeline configuration"
    - "conditional parallel coordinates"
    - "atmseer increasing transparency and controllability in automated machine learning"
    - "human ai collaboration in data science exploring data scientists perceptions of automated ai"
    - "survey on automated machine learning"

Connected through hyperedge HE-3 (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"
    - "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-4 (induced by source paper "distal explanations for explainable reinforcement learning agents"):
  with members:
    - "explainable reinforcement learning through a causal lens"
    - "conservative q improvement reinforcement learning for an interpretable decision tree policy"
    - "contrastive explanation a structural model approach"
    - "contrastive explanations for reinforcement learning in terms of expected consequences"
    - "autonomous self explanation of behavior for interactive reinforcement learning agents"
    - "explanation in artificial intelligence insights from the social sciences"
    - "starcraft ii a new challenge for reinforcement learning"

Connected through hyperedge HE-5 (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"
    - "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"
    - "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-6 (induced by source paper "how to answer why evaluating the explanations of ai through mental model analysis"):
  with members:
    - "measuring the quality of explanations the system causability scale scs comparing human and machine explanations"

Connected through hyperedge HE-7 (induced by source paper "a multidisciplinary survey and framework for design and evaluation of explainable ai systems"):
  with members:
    - "fairsight visual analytics for fairness in decision making"
    - "predicting model failure using saliency maps in autonomous driving systems"
    - "human grounded evaluations of explanation methods for text classification"
    - "quantifying interpretability and trust in machine learning systems"
    - "summit scaling deep learning interpretability by visualizing activation and attribution summarizations"
    - "explainable artificial intelligence xai concepts taxonomies opportunities and challenges toward responsible ai"
    - "open issues in combating fake news interpretability as an opportunity"
    - "a survey on bias and fairness in machine learning"

Connected through hyperedge HE-8 (induced by source paper "explainable deep learning for video recognition tasks a framework recommendations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "discriminating spatial and temporal relevance in deep taylor decompositions for explainable activity recognition"
    - "real world anomaly detection in surveillance videos"
    - "interpretable to whom a role based model for analyzing interpretable machine learning systems"
    - "evaluating feature importance estimates"
    - "tip typifying the interpretability of procedures"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning spatio temporal features with 3d residual networks for action recognition"

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)