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: "fairness and accountability design needs for algorithmic support in high stakes public sector decision making"
  Abstract: Calls for heightened consideration of fairness and accountability in algorithmically-informed public decisions-like taxation, justice, and child protection-are now commonplace. How might designers support such human values? We interviewed 27 public sector machine learning practitioners across 5 OECD countries regarding challenges understanding and imbuing public values into their work. The results suggest a disconnect between organisational and institutional realities, constraints and needs, and those addressed by current research into usable, transparent and 'discrimination-aware' machine learning-absences likely to undermine practical initiatives unless addressed. We see design opportunities in this disconnect, such as in supporting the tracking of concept drift in secondary data sources, and in building usable transparency tools to identify risks and incorporate domain knowledge, aimed both at managers and at the 'street-level bureaucrats' on the frontlines of public service. We conclude by outlining ethical challenges and future directions for collaboration in these high-stakes applications.

Connected through hyperedge HE-1 (induced by source paper "factors influencing perceived fairness in algorithmic decision making algorithm outcomes development procedures and individual differences"):
  with members:
    - "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "society in the loop programming the algorithmic social contract"
    - "the frontiers of fairness in machine learning"
    - "manipulating and measuring model interpretability"
    - "a reductions approach to fair classification"
    - "demographics and discussion influence views on algorithmic fairness"
    - "network dissection quantifying interpretability of deep visual representations"

Connected through hyperedge HE-2 (induced by source paper "keeping community in the loop understanding wikipedia stakeholder values for machine learning based systems"):
  with members:
    - "the frontiers of fairness in machine learning"
    - "an empirical study of rich subgroup fairness for machine learning"
    - "on fairness and calibration"
    - "network dissection quantifying interpretability of deep visual representations"
    - "explanation in artificial intelligence insights from the social sciences"
    - "operationalizing conflict and cooperation between automated software agents in wikipedia a replication and expansion of even good bots fight"
    - "algorithmic decision making and the cost of fairness"
    - "generating visual explanations"

Connected through hyperedge HE-3 (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"
    - "explaining models an empirical study of how explanations impact fairness judgment"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "explanation in artificial intelligence insights from the social sciences"
    - "accountability of ai under the law the role of explanation"

Connected through hyperedge HE-4 (induced by source paper "how to support users in understanding intelligent systems structuring the discussion"):
  with members:
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-5 (induced by source paper "designing fair ai for managing employees in organizations a review critique and design agenda"):
  with members:
    - "how do fairness definitions fare examining public attitudes towards algorithmic definitions of fairness"

Connected through hyperedge HE-6 (induced by source paper "understanding artificial intelligence ethics and safety"):
  with members:
    - "a roadmap toward the resilient internet of things for cyber physical systems"
    - "a grounded interaction protocol for explainable artificial intelligence"
    - "automated rationale generation a technique for explainable ai and its effects on human perceptions"
    - "model cards for model reporting"
    - "a framework for understanding unintended consequences of machine learning"
    - "spectrum data poisoning with adversarial deep learning"
    - "explaining explanations in ai"
    - "problem formulation and fairness"

Connected through hyperedge HE-7 (induced by source paper "improving fairness in machine learning systems what do industry practitioners need"):
  with members:
    - "delayed impact of fair machine learning"
    - "investigating human machine complementarity for recidivism predictions"
    - "assessing and addressing algorithmic bias but before we get there"
    - "the externalities of exploration and how data diversity helps exploitation"
    - "fairness under composition"
    - "datasheets for datasets"
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"
    - "a reductions approach to fair classification"

Connected through hyperedge HE-8 (induced by source paper "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning"):
  with members:
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices"
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"
    - "a moral framework for understanding of fair ml through economic models of equality of opportunity"
    - "how do fairness definitions fare examining public attitudes towards algorithmic definitions of fairness"
    - "manipulating and measuring model interpretability"
    - "a voting based system for ethical decision making"
    - "on formalizing fairness in prediction with machine learning"

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)