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 "a human in the loop framework to construct context dependent mathematical formulations of fairness"):
  Abstract: Despite the recent surge of interest in designing and guaranteeing mathematical formulations of fairness, virtually all existing notions of algorithmic fairness fail to be adaptable to the intricacies and nuances of the decision-making context at hand. We argue that capturing such factors is an inherently human task, as it requires knowledge of the social background in which machine learning tools impact real people's outcomes and a deep understanding of the ramifications of automated decisions for decision subjects and society. In this work, we present a framework to construct a context-dependent mathematical formulation of fairness utilizing people's judgment of fairness. We utilize the theoretical model of Heidari et al. (2019)---which shows that most existing formulations of algorithmic fairness are special cases of economic models of Equality of Opportunity (EOP)---and present a practical human-in-the-loop approach to pinpoint the fairness notion in the EOP family that best captures people's perception of fairness in the given context. To illustrate our framework, we run human-subject experiments designed to learn the parameters of Heidari et al.'s EOP model (including circumstance, desert, and utility) in a hypothetical recidivism decision-making scenario. Our work takes an initial step toward democratizing the formulation of fairness and utilizing human-judgment to tackle a fundamental shortcoming of automated decision-making systems: that the machine on its own is incapable of understanding and processing the human aspects and social context of its decisions.

Includes members:
  Member "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "algorithmic fairness")
  Member "improving fairness in machine learning systems what do industry practitioners need":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "algorithmic fairness")
  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 "fairness and accountability design needs for algorithmic support in high stakes public sector decision making":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning")
  Member "equality of opportunity in supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "algorithmic fairness")

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.AI(Artificial Intelligence)