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: "proxy non discrimination in data driven systems"
  Abstract: Machine learnt systems inherit biases against protected classes, historically disparaged groups, from training data. Usually, these biases are not explicit, they rely on subtle correlations discovered by training algorithms, and are therefore difficult to detect. We formalize proxy discrimination in data-driven systems, a class of properties indicative of bias, as the presence of protected class correlates that have causal influence on the system's output. We evaluate an implementation on a corpus of social datasets, demonstrating how to validate systems against these properties and to repair violations where they occur.

Connected through hyperedge HE-1 (induced by source paper "fairsight visual analytics for fairness in decision making"):
  with members:
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "manifold a model agnostic framework for interpretation and diagnosis of machine learning models"
    - "a nutritional label for rankings"
    - "using image fairness representations in diversity based re ranking for recommendations"
    - "rulematrix visualizing and understanding classifiers with rules"
    - "equity of attention amortizing individual fairness in rankings"
    - "fairness of exposure in rankings"
    - "fa ir a fair top k ranking algorithm"

Connected through hyperedge HE-2 (induced by source paper "fliptest fairness testing via optimal transport"):
  with members:
    - "adversarial computation of optimal transport maps"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "fairness under composition"
    - "scalable unbalanced optimal transport using generative adversarial networks"
    - "approximating optimal transport with linear programs"
    - "automated test generation to detect individual discrimination in ai models"
    - "hunting for discriminatory proxies in linear regression models"
    - "an empirical study of rich subgroup fairness for machine learning"

Connected through hyperedge HE-3 (induced by source paper "repairing without retraining avoiding disparate impact with counterfactual distributions"):
  with members:
    - "algorithmic fairness"
    - "from soft classifiers to hard decisions how fair can we be"
    - "on the direction of discrimination an information theoretic analysis of disparate impact in machine learning"
    - "a reductions approach to fair classification"
    - "on fairness and calibration"
    - "fairness testing testing software for discrimination"
    - "algorithmic decision making and the cost of fairness"
    - "equality of opportunity in supervised learning"

Connected through hyperedge HE-4 (induced by source paper "bias in bias out evaluating the folk wisdom"):
  with members:
    - "algorithmic fairness"
    - "discrimination in the age of algorithms"
    - "downstream effects of affirmative action"
    - "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"
    - "inherent trade offs in the fair determination of risk scores"
    - "fairness through awareness"

Connected through hyperedge HE-5 (induced by source paper "the disparate effects of strategic manipulation"):
  with members:
    - "how do classifiers induce agents to invest effort strategically"
    - "the social cost of strategic classification"
    - "strategic classification from revealed preferences"
    - "runaway feedback loops in predictive policing"
    - "causal discrimination discovery through propensity score analysis"
    - "price of transparency in strategic machine learning"
    - "discrimination in online ad delivery"

Connected through hyperedge HE-6 (induced by source paper "learning fair representations for kernel models"):
  with members:
    - "learning adversarially fair and transferable representations"
    - "hunting for discriminatory proxies in linear regression models"
    - "learning controllable fair representations"
    - "equality of opportunity in supervised learning"
    - "censoring representations with an adversary"

Connected through hyperedge HE-7 (induced by source paper "the measure and mismeasure of fairness a critical review of fair machine learning"):
  with members:
    - "delayed impact of fair machine learning"
    - "a short term intervention for long term fairness in the labor market"
    - "on fairness and calibration"
    - "interventions over predictions reframing the ethical debate for actuarial risk assessment"
    - "algorithmic decision making and the cost of fairness"
    - "exposing the probabilistic causal structure of discrimination"
    - "causal discrimination discovery through propensity score analysis"
    - "equality of opportunity in supervised learning"

Connected through hyperedge HE-8 (induced by source paper "hunting for discriminatory proxies in linear regression models"):
  with members:
    - "use privacy in data driven systems theory and experiments with machine learnt programs"
    - "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.CY(Computers and Society)