human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "downstream effects of affirmative action"
  Abstract: We study a two-stage model, in which students are 1) admitted to college on the basis of an entrance exam which is a noisy signal about their qualifications (type), and then 2) those students who were admitted to college can be hired by an employer as a function of their college grades, which are an independently drawn noisy signal of their type. Students are drawn from one of two populations, which might have different type distributions. We assume that the employer at the end of the pipeline is rational, in the sense that it computes a posterior distribution on student type conditional on all information that it has available (college admissions, grades, and group membership), and makes a decision based on posterior expectation. We then study what kinds of fairness goals can be achieved by the college by setting its admissions rule and grading policy. For example, the college might have the goal of guaranteeing equal opportunity across populations: that the probability of passing through the pipeline and being hired by the employer should be independent of group membership, conditioned on type. Alternately, the college might have the goal of incentivizing the employer to have a group blind hiring rule. We show that both goals can be achieved when the college does not report grades. On the other hand, we show that under reasonable conditions, these goals are impossible to achieve even in isolation when the college uses an (even minimally) informative grading policy.

Connected through hyperedge HE-1 (induced by source paper "fairness in learning based sequential decision algorithms a survey"):
  with members:
    - "achieving fairness in the stochastic multi armed bandit problem"
    - "fair contextual multi armed bandits theory and experiments"
    - "on the long term impact of algorithmic decision policies effort unfairness and feature segregation through social learning"
    - "the disparate equilibria of algorithmic decision making when individuals invest rationally"
    - "a survey on bias and fairness in machine learning"
    - "fairness with dynamics"
    - "group retention when using machine learning in sequential decision making the interplay between user dynamics and fairness"
    - "combinatorial sleeping bandits with fairness constraints"

Connected through hyperedge HE-2 (induced by source paper "pipeline interventions"):
  with members:
    - "the disparate equilibria of algorithmic decision making when individuals invest rationally"
    - "delayed impact of fair machine learning"
    - "a short term intervention for long term fairness in the labor market"
    - "fairness under composition"
    - "from fair decision making to social equality"
    - "the frontiers of fairness in machine learning"
    - "gender productivity and prestige in computer science faculty hiring networks"

Connected through hyperedge HE-3 (induced by source paper "tracking and improving information in the service of fairness"):
  with members:
    - "algorithmic fairness"
    - "50 years of test un fairness lessons for machine learning"
    - "delayed impact of fair machine learning"
    - "facebook s advertising platform new attack vectors and the need for interventions"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "the price of fair pca one extra dimension"
    - "access to population level signaling as a source of inequality"
    - "fairness through computationally bounded awareness"

Connected through hyperedge HE-4 (induced by source paper "causal modeling for fairness in dynamical systems"):
  with members:
    - "counterfactual off policy evaluation with gumbel max structural causal models"
    - "understanding agent incentives using causal influence diagrams part i single action settings"
    - "the disparate effects of strategic manipulation"
    - "a short term intervention for long term fairness in the labor market"
    - "the social cost of strategic classification"
    - "woulda coulda shoulda counterfactually guided policy search"
    - "fairness through causal awareness learning latent variable models for biased data"
    - "from fair decision making to social equality"

Connected through hyperedge HE-5 (induced by source paper "on the long term impact of algorithmic decision policies effort unfairness and feature segregation through social learning"):
  with members:
    - "the disparate effects of strategic manipulation"
    - "delayed impact of fair machine learning"
    - "the social cost of strategic classification"
    - "a moral framework for understanding of fair ml through economic models of equality of opportunity"
    - "fairness behind a veil of ignorance a welfare analysis for automated decision making"
    - "strategic classification from revealed preferences"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "algorithmic decision making and the cost of fairness"

Connected through hyperedge HE-6 (induced by source paper "efficient fair principal component analysis"):
  with members:
    - "flexibly fair representation learning by disentanglement"
    - "fair dimensionality reduction and iterative rounding for sdps"
    - "convex formulations for fair principal component analysis"
    - "the price of fair pca one extra dimension"
    - "fairness under composition"
    - "a reductions approach to fair classification"
    - "eliminating latent discrimination train then mask"
    - "on fairness and calibration"

Connected through hyperedge HE-7 (induced by source paper "bias in bias out evaluating the folk wisdom"):
  with members:
    - "algorithmic fairness"
    - "discrimination in the age of algorithms"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "learning under selective labels in the presence of expert consistency"
    - "fairness through causal awareness learning latent variable models for biased data"
    - "proxy non discrimination in data driven systems"
    - "inherent trade offs in the fair determination of risk scores"
    - "fairness through awareness"

Connected through hyperedge HE-8 (induced by source paper "on the legal compatibility of fairness definitions"):
  with members:
    - "interpreting social respect a normative lens for ml models"
    - "eliciting and enforcing subjective individual fairness"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "differentially private fair learning"
    - "equality of opportunity in supervised learning"
    - "on the im possibility of fairness"
    - "fairness through awareness"

Question: Please tell me which class the center node 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.GT(Computer Science and Game Theory)