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 "measurement and fairness"):
  Abstract: We introduce the language of measurement modeling from the quantitative social sciences as a framework for understanding fairness in computational systems. Computational systems often involve unobservable theoretical constructs, such as "creditworthiness," "teacher quality," or "risk to society," that cannot be measured directly and must instead be inferred from observable properties thought to be related to them---i.e., operationalized via a measurement model. This process introduces the potential for mismatch between the theoretical understanding of the construct purported to be measured and its operationalization. Indeed, we argue that many of the harms discussed in the literature on fairness in computational systems are direct results of such mismatches. Further complicating these discussions is the fact that fairness itself is an unobservable theoretical construct. Moreover, it is an essentially contested construct---i.e., it has many different theoretical understandings depending on the context. We argue that this contestedness underlies recent debates about fairness definitions: disagreements that appear to be about contradictory operationalizations are, in fact, disagreements about different theoretical understandings of the construct itself. By introducing the language of measurement modeling, we provide the computer science community with a process for making explicit and testing assumptions about unobservable theoretical constructs, thereby making it easier to identify, characterize, and even mitigate fairness-related harms.

Includes members:
  Member "50 years of test un fairness lessons for machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "demographic bias in biometrics a survey on an emerging challenge")
  Member "the scientific method in the science of machine learning":
    also connected through other hyperedges:
      (none)
  Member "the profiling potential of computer vision and the challenge of computational empiricism":
    also connected through other hyperedges:
      (none)
  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 "problem formulation and fairness":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "no computation without representation avoiding data and algorithm biases through diversity")
  Member "improving fairness in machine learning systems what do industry practitioners need":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "algorithmic fairness")
  Member "simplicity creates inequity implications for fairness stereotypes and interpretability":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a resolution in algorithmic fairness calibrated scores for fair classifications")
  Member "the measure and mismeasure of fairness a critical review of fair machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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)