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: "algorithmic fairness"
  Abstract: An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence (AI) algorithms in spheres ranging from healthcare, transportation, and education to college admissions, recruitment, provision of loans and many more realms. Since they now touch on many aspects of our lives, it is crucial to develop AI algorithms that are not only accurate but also objective and fair. Recent studies have shown that algorithmic decision-making may be inherently prone to unfairness, even when there is no intention for it. This paper presents an overview of the main concepts of identifying, measuring and improving algorithmic fairness when using AI algorithms. The paper begins by discussing the causes of algorithmic bias and unfairness and the common definitions and measures for fairness. Fairness-enhancing mechanisms are then reviewed and divided into pre-process, in-process and post-process mechanisms. A comprehensive comparison of the mechanisms is then conducted, towards a better understanding of which mechanisms should be used in different scenarios. The paper then describes the most commonly used fairness-related datasets in this field. Finally, the paper ends by reviewing several emerging research sub-fields of algorithmic fairness.

Connected through hyperedge HE-1 (induced by source paper "to split or not to split the impact of disparate treatment in classification"):
  with members:
    - "recovering from biased data can fairness constraints improve accuracy"
    - "repairing without retraining avoiding disparate impact with counterfactual distributions"
    - "a framework for understanding unintended consequences of machine learning"
    - "optimized score transformation for fair classification"
    - "fairness with minimal harm a pareto optimal approach for healthcare"
    - "obfuscation via information density estimation"
    - "welfare and distributional impacts of fair classification"
    - "multiaccuracy black box post processing for fairness in classification"

Connected through hyperedge HE-2 (induced by source paper "bias in bios a case study of semantic representation bias in a high stakes setting"):
  with members:
    - "gender bias in coreference resolution"
    - "equity of attention amortizing individual fairness in rankings"
    - "fairness under composition"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "gender bias in coreference resolution evaluation and debiasing methods"
    - "attention is all you need"
    - "racial disparity in natural language processing a case study of social media african american english"
    - "advances in pre training distributed word representations"

Connected through hyperedge HE-3 (induced by source paper "a framework for understanding unintended consequences of machine learning"):
  with members:
    - "model cards for model reporting"
    - "a broader view on bias in automated decision making reflecting on epistemology and dynamics"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "learning tasks for multitask learning heterogenous patient populations in the icu"
    - "a convex framework for fair regression"
    - "on formalizing fairness in prediction with machine learning"
    - "algorithmic decision making and the cost of fairness"
    - "decoupled classifiers for fair and efficient machine learning"

Connected through hyperedge HE-4 (induced by source paper "tracking and improving information in the service of fairness"):
  with members:
    - "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"
    - "downstream effects of affirmative action"
    - "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-5 (induced by source paper "repairing without retraining avoiding disparate impact with counterfactual distributions"):
  with members:
    - "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"
    - "proxy non discrimination in data driven systems"
    - "equality of opportunity in supervised learning"

Connected through hyperedge HE-6 (induced by source paper "designing interfaces to help stakeholders comprehend navigate and manage algorithmic trade offs"):
  with members:
    - "explaining explanations an overview of interpretability of machine learning"
    - "an empirical study of rich subgroup fairness for machine learning"
    - "a reductions approach to fair classification"
    - "on fairness and calibration"
    - "explanation in artificial intelligence insights from the social sciences"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "algorithmic decision making and the cost of fairness"
    - "fairness in learning classic and contextual bandits"

Connected through hyperedge HE-7 (induced by source paper "bias in bias out evaluating the folk wisdom"):
  with members:
    - "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"
    - "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 "experiential ai"):
  with members:
    - "fairness and accountability design needs for algorithmic support in high stakes public sector decision making"
    - "why should i trust you explaining the predictions of any classifier"

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)