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 "affirmative action policies for top k candidates selection with an application to the design of policies for university admissions"):
  Abstract: We consider the problem of designing affirmative action policies for selecting the top-k candidates from a pool of applicants. We assume that for each candidate we have socio-demographic attributes and a series of variables that serve as indicators of future performance (e.g., results on standardized tests). We further assume that we have access to historical data including the actual performance of previously selected candidates. Critically, performance information is only available for candidates who were selected under some previous selection policy. #R##N#In this work we assume that due to legal requirements or voluntary commitments, an organization wants to increase the presence of people from disadvantaged socio-demographic groups among the selected candidates. Hence, we seek to design an affirmative action or positive action policy. This policy has two concurrent objectives: (i) to select candidates who, given what can be learnt from historical data, are more likely to perform well, and (ii) to select candidates in a way that increases the representation of disadvantaged socio-demographic groups. #R##N#Our motivating application is the design of university admission policies to bachelor's degrees. We use a causal model as a framework to describe several families of policies (changing component weights, giving bonuses, and enacting quotas), and compare them both theoretically and through extensive experimentation on a large real-world dataset containing thousands of university applicants. Our paper is the first to place the problem of affirmative-action policy design within the framework of algorithmic fairness. Our empirical results indicate that simple policies could favor the admission of disadvantaged groups without significantly compromising on the quality of accepted candidates.

Includes members:
  Member "downstream effects of affirmative action":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fairness in learning based sequential decision algorithms a survey")
  Member "a short term intervention for long term fairness in the labor market":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "algorithmic fairness")
  Member "from fair decision making to social equality":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fairness in learning based sequential decision algorithms a survey")
  Member "ranking with fairness constraints":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fair correlation clustering")
  Member "fa ir a fair top k ranking algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fairsight visual analytics for fairness in decision making")
  Member "equality of opportunity in supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "algorithmic fairness")
  Member "fairness through awareness":
    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)