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 "towards a critical race methodology in algorithmic fairness"):
  Abstract: We examine the way race and racial categories are adopted in algorithmic fairness frameworks. Current methodologies fail to adequately account for the socially constructed nature of race, instead adopting a conceptualization of race as a fixed attribute. Treating race as an attribute, rather than a structural, institutional, and relational phenomenon, can serve to minimize the structural aspects of algorithmic unfairness. In this work, we focus on the history of racial categories and turn to critical race theory and sociological work on race and ethnicity to ground conceptualizations of race for fairness research, drawing on lessons from public health, biomedical research, and social survey research. We argue that algorithmic fairness researchers need to take into account the multidimensionality of race, take seriously the processes of conceptualizing and operationalizing race, focus on social processes which produce racial inequality, and consider perspectives of those most affected by sociotechnical systems.

Includes members:
  Member "model cards for model reporting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "explainability fact sheets a framework for systematic assessment of explainable approaches")
  Member "racial categories in machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "theory in theory out how social theory can solve problems that machine learning can t")
  Member "measurement and fairness":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a hierarchy of limitations in machine learning")
  Member "the measure and mismeasure of fairness a critical review of fair machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "algorithmic fairness")
  Member "interventions over predictions reframing the ethical debate for actuarial risk assessment":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a hierarchy of limitations in machine learning")
  Member "equality of opportunity in supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "algorithmic fairness")
  Member "inherent trade offs in the fair determination of risk scores":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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.CY(Computers and Society)