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: "explaining models an empirical study of how explanations impact fairness judgment"
  Abstract: Ensuring fairness of machine learning systems is a human-in-the-loop process. It relies on developers, users, and the general public to identify fairness problems and make improvements. To facilitate the process we need effective, unbiased, and user-friendly explanations that people can confidently rely on. Towards that end, we conducted an empirical study with four types of programmatically generated explanations to understand how they impact people's fairness judgments of ML systems. With an experiment involving more than 160 Mechanical Turk workers, we show that: 1) Certain explanations are considered inherently less fair, while others can enhance people's confidence in the fairness of the algorithm; 2) Different fairness problems-such as model-wide fairness issues versus case-specific fairness discrepancies-may be more effectively exposed through different styles of explanation; 3) Individual differences, including prior positions and judgment criteria of algorithmic fairness, impact how people react to different styles of explanation. We conclude with a discussion on providing personalized and adaptive explanations to support fairness judgments of ML systems.

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"
    - "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 "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"
    - "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-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"
    - "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-4 (induced by source paper "evidence based explanation to promote fairness in ai systems"):
  with members:
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "fairness and accountability design needs for algorithmic support in high stakes public sector decision making"
    - "explanation in artificial intelligence insights from the social sciences"
    - "accountability of ai under the law the role of explanation"

Connected through hyperedge HE-5 (induced by source paper "do i look like a criminal examining how race presentation impacts human judgement of recidivism"):
  with members:
    - "what you see is what you get the impact of representation criteria on human bias in hiring"
    - "investigating human machine complementarity for recidivism predictions"
    - "semantics derived automatically from language corpora contain human like biases"

Connected through hyperedge HE-6 (induced by source paper "fairness and decision making in collaborative shift scheduling systems"):
  with members:
    - "on the apparent conflict between individual and group fairness"
    - "fairness in machine learning lessons from political philosophy"
    - "inherent trade offs in the fair determination of risk scores"

Connected through hyperedge HE-7 (induced by source paper "in ai we trust factors that influence trustworthiness of ai infused decision making processes"):
  with members:
    - "one explanation does not fit all a toolkit and taxonomy of ai explainability techniques"
    - "the price of interpretability"
    - "survey on automated machine learning"
    - "metrics for explainable ai challenges and prospects"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "taking human out of learning applications a survey on automated machine learning"
    - "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.HC(Human-Computer Interaction)