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: "the global landscape of ai ethics guidelines"
  Abstract: In the past five years, private companies, research institutions and public sector organizations have issued principles and guidelines for ethical artificial intelligence (AI). However, despite an apparent agreement that AI should be ‘ethical’, there is debate about both what constitutes ‘ethical AI’ and which ethical requirements, technical standards and best practices are needed for its realization. To investigate whether a global agreement on these questions is emerging, we mapped and analysed the current corpus of principles and guidelines on ethical AI. Our results reveal a global convergence emerging around five ethical principles (transparency, justice and fairness, non-maleficence, responsibility and privacy), with substantive divergence in relation to how these principles are interpreted, why they are deemed important, what issue, domain or actors they pertain to, and how they should be implemented. Our findings highlight the importance of integrating guideline-development efforts with substantive ethical analysis and adequate implementation strategies.

Connected through hyperedge HE-1 (induced by source paper "ai safety state of the field through quantitative lens"):
  with members:
    - "fairness in learning based sequential decision algorithms a survey"
    - "dave i can assure you that it s going to be all right a definition case for and survey of algorithmic assurances in human autonomy trust relationships"
    - "explainability in human agent systems"
    - "classification schemas for artificial intelligence failures"
    - "explainable artificial intelligence xai concepts taxonomies opportunities and challenges toward responsible ai"
    - "a survey on bias and fairness in machine learning"
    - "machine learning testing survey landscapes and horizons"
    - "unexplainability and incomprehensibility of artificial intelligence"

Connected through hyperedge HE-2 (induced by source paper "closing the ai accountability gap defining an end to end framework for internal algorithmic auditing"):
  with members:
    - "principles alone cannot guarantee ethical ai"
    - "principles alone cannot guarantee ethical ai"
    - "diversity in faces"
    - "model cards for model reporting"
    - "evolutionary computation and ai safety research problems impeding routine and safe real world application of evolution"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "a broader view on bias in automated decision making reflecting on epistemology and dynamics"
    - "examining gender and race bias in two hundred sentiment analysis systems"

Connected through hyperedge HE-3 (induced by source paper "ethics of food recommender applications"):
  with members:
    - "the unfairness of popularity bias in recommendation"
    - "from what to how an overview of ai ethics tools methods and research to translate principles into practices"
    - "the ethics of ai ethics an evaluation of guidelines"
    - "from what to how an initial review of publicly available ai ethics tools methods and research to translate principles into practices"
    - "building jiminy cricket an architecture for moral agreements among stakeholders"
    - "explainable recommendation a survey and new perspectives"

Connected through hyperedge HE-4 (induced by source paper "principles alone cannot guarantee ethical ai"):
  with members:
    - "from what to how an overview of ai ethics tools methods and research to translate principles into practices"
    - "the ethics of ai ethics an evaluation of guidelines"
    - "improving fairness in machine learning systems what do industry practitioners need"

Connected through hyperedge HE-5 (induced by source paper "ethics in the digital era"):
  with members:
    - "solidarity should be a core ethical principle of ai"
    - "the role of artificial intelligence in achieving the sustainable development goals"

Connected through hyperedge HE-6 (induced by source paper "the ethics of ai ethics an evaluation of guidelines"):
  with members:
    - "principles alone cannot guarantee ethical ai"
    - "principles alone cannot guarantee ethical ai"
    - "tackling climate change with machine learning"
    - "from what to how an overview of ai ethics tools methods and research to translate principles into practices"
    - "explaining explanations in ai"
    - "forbidden knowledge in machine learning reflections on the limits of research and publication"
    - "the malicious use of artificial intelligence forecasting prevention and mitigation"
    - "on quantifying and understanding the role of ethics in ai research a historical account of flagship conferences and journals"

Connected through hyperedge HE-7 (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"
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"
    - "questioning the assumptions behind fairness solutions"
    - "enslaving the algorithm from a right to an explanation to a right to better decisions"
    - "manipulating and measuring model interpretability"
    - "the dataset nutrition label a framework to drive higher data quality standards"

Connected through hyperedge HE-8 (induced by source paper "about ml annotation and benchmarking on understanding and transparency of machine learning lifecycles"):
  with members:
    - "principles alone cannot guarantee ethical ai"
    - "principles alone cannot guarantee ethical ai"
    - "model cards for model reporting"
    - "towards standardization of data licenses the montreal data license"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "datasheets for datasets"
    - "the dataset nutrition label a framework to drive higher data quality standards"
    - "linking artificial intelligence principles"

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.CY(Computers and Society)