human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning"):
  Abstract: Fairness for Machine Learning has received considerable attention, recently. Various mathematical formulations of fairness have been proposed, and it has been shown that it is impossible to satisfy all of them simultaneously. The literature so far has dealt with these impossibility results by quantifying the tradeoffs between different formulations of fairness. Our work takes a different perspective on this issue. Rather than requiring all notions of fairness to (partially) hold at the same time, we ask which one of them is the most appropriate given the societal domain in which the decision-making model is to be deployed. We take a descriptive approach and set out to identify the notion of fairness that best captures \emph{lay people's perception of fairness}. We run adaptive experiments designed to pinpoint the most compatible notion of fairness with each participant's choices through a small number of tests. Perhaps surprisingly, we find that the most simplistic mathematical definition of fairness---namely, demographic parity---most closely matches people's idea of fairness in two distinct application scenarios. This conclusion remains intact even when we explicitly tell the participants about the alternative, more complicated definitions of fairness, and we reduce the cognitive burden of evaluating those notions for them. Our findings have important implications for the Fair ML literature and the discourse on formalizing algorithmic fairness.

Includes members:
  Member "improving fairness in machine learning systems what do industry practitioners need":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "algorithmic fairness")
  Member "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "noise tolerant fair classification")
  Member "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "questioning the ai informing design practices for explainable ai user experiences")
  Member "a moral framework for understanding of fair ml through economic models of equality of opportunity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "no computation without representation avoiding data and algorithm biases through diversity")
  Member "how do fairness definitions fare examining public attitudes towards algorithmic definitions of fairness":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "designing fair ai for managing employees in organizations a review critique and design agenda")
  Member "manipulating and measuring model interpretability":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "questioning the ai informing design practices for explainable ai user experiences")
  Member "fairness and accountability design needs for algorithmic support in high stakes public sector decision making":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "factors influencing perceived fairness in algorithmic decision making algorithm outcomes development procedures and individual differences")
  Member "a voting based system for ethical decision making":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "privacy preserving crowd guided ai decision making in ethical dilemmas")

Question: Please tell me which class the source paper 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)