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: "enslaving the algorithm from a right to an explanation to a right to better decisions"
  Abstract: As concerns about unfairness and discrimination in “black box” machine learning systems rise, a legal “right to an explanation” has emerged as a compellingly attractive approach for challenge and redress. We outline recent debates on the limited provisions in European data protection law, and introduce and analyze newer explanation rights in French administrative law and the draft modernized Council of Europe Convention 108. While individual rights can be useful, in privacy law they have historically unreasonably burdened the average data subject. “Meaningful information” about algorithmic logics is more technically possible than commonly thought, but this exacerbates a new “transparency fallacy”—an illusion of remedy rather than anything substantively helpful. While rights-based approaches deserve a firm place in the toolbox, other forms of governance, such as impact assessments, “soft law,” judicial review, and model repositories deserve more attention, alongside catalyzing agencies acting for users to control algorithmic system design.

Connected through hyperedge HE-1 (induced by source paper "lifting interpretability performance trade off via automated feature engineering"):
  with members:
    - "the autofeat python library for automatic feature engineering and selection"
    - "magix model agnostic globally interpretable explanations"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "evaluation of a tree based pipeline optimization tool for automating data science"
    - "xgboost a scalable tree boosting system"
    - "openml networked science in machine learning"

Connected through hyperedge HE-2 (induced by source paper "from what to how an initial review of publicly available ai ethics tools methods and research to translate principles into practices"):
  with members:
    - "ethically aligned design of autonomous systems industry viewpoint and an empirical study"
    - "model cards for model reporting"
    - "the ethics of ai ethics an evaluation of guidelines"
    - "the global landscape of ai ethics guidelines"
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"
    - "questioning the assumptions behind fairness solutions"
    - "manipulating and measuring model interpretability"
    - "the dataset nutrition label a framework to drive higher data quality standards"

Connected through hyperedge HE-3 (induced by source paper "do not trust additive explanations"):
  with members:
    - "on the in fidelity and sensitivity for explanations"
    - "consistent individualized feature attribution for tree ensembles"
    - "on the robustness of interpretability methods"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"
    - "why should i trust you explaining the predictions of any classifier"
    - "openml networked science in machine learning"

Connected through hyperedge HE-4 (induced by source paper "ibreakdown uncertainty of model explanations for non additive predictive models"):
  with members:
    - "consistent individualized feature attribution for tree ensembles"
    - "assessing the stability of interpretable models"
    - "on the robustness of interpretability methods"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "learning important features through propagating activation differences"
    - "why should i trust you explaining the predictions of any classifier"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-5 (induced by source paper "safe ml surrogate assisted feature extraction for model learning"):
  with members:
    - "magix model agnostic globally interpretable explanations"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "evaluation of a tree based pipeline optimization tool for automating data science"
    - "xgboost a scalable tree boosting system"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-6 (induced by source paper "some hci priorities for gdpr compliant machine learning"):
  with members:
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"

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)