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: "fairness under composition"
  Abstract: Algorithmic fairness, and in particular the fairness of scoring and classification algorithms, has become a topic of increasing social concern and has recently witnessed an explosion of research in theoretical computer science, machine learning, statistics, the social sciences, and law. Much of the literature considers the case of a single classifier (or scoring function) used once, in isolation. In this work, we initiate the study of the fairness properties of systems composed of algorithms that are fair in isolation; that is, we study fairness under composition. We identify pitfalls of naive composition and give general constructions for fair composition, demonstrating both that classifiers that are fair in isolation do not necessarily compose into fair systems and also that seemingly unfair components may be carefully combined to construct fair systems. We focus primarily on the individual fairness setting proposed in [Dwork, Hardt, Pitassi, Reingold, Zemel, 2011], but also extend our results to a large class of group fairness definitions popular in the recent literature, exhibiting several cases in which group fairness definitions give misleading signals under composition.

Connected through hyperedge HE-1 (induced by source paper "algorithmic fairness"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "fairface face attribute dataset for balanced race gender and age"
    - "fair regression quantitative definitions and reduction based algorithms"
    - "putting fairness principles into practice challenges metrics and improvements"
    - "generative adversarial networks for mitigating biases in machine learning systems"
    - "improved adversarial learning for fair classification"
    - "compositional fairness constraints for graph embeddings"
    - "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning"

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"
    - "downstream effects of affirmative action"
    - "a short term intervention for long term fairness in the labor market"
    - "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 "practical compositional fairness understanding fairness in multi task ml systems"):
  with members:
    - "fairness aware ranking in search recommendation systems with application to linkedin talent search"
    - "putting fairness principles into practice challenges metrics and improvements"
    - "nuanced metrics for measuring unintended bias with real data for text classification"
    - "fairness in recommendation ranking through pairwise comparisons"
    - "the fairness of risk scores beyond classification bipartite ranking and the xauc metric"
    - "pairwise fairness for ranking and regression"
    - "training well generalizing classifiers for fairness metrics and other data dependent constraints"
    - "multiaccuracy black box post processing for fairness in classification"

Connected through hyperedge HE-4 (induced by source paper "bias in bios a case study of semantic representation bias in a high stakes setting"):
  with members:
    - "algorithmic fairness"
    - "gender bias in coreference resolution"
    - "equity of attention amortizing individual fairness in rankings"
    - "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-5 (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"
    - "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"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"

Connected through hyperedge HE-6 (induced by source paper "putting fairness principles into practice challenges metrics and improvements"):
  with members:
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "delayed impact of fair machine learning"
    - "counterfactual fairness in text classification through robustness"
    - "mitigating unwanted biases with adversarial learning"
    - "learning adversarially fair and transferable representations"
    - "a reductions approach to fair classification"
    - "penalizing unfairness in binary classification"
    - "data decisions and theoretical implications when adversarially learning fair representations"

Connected through hyperedge HE-7 (induced by source paper "improving fairness in machine learning systems what do industry practitioners need"):
  with members:
    - "delayed impact of fair machine learning"
    - "investigating human machine complementarity for recidivism predictions"
    - "assessing and addressing algorithmic bias but before we get there"
    - "the externalities of exploration and how data diversity helps exploitation"
    - "datasheets for datasets"
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"
    - "fairness and accountability design needs for algorithmic support in high stakes public sector decision making"
    - "a reductions approach to fair classification"

Connected through hyperedge HE-8 (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"
    - "downstream effects of affirmative action"
    - "convex formulations for fair principal component analysis"
    - "the price of fair pca one extra dimension"
    - "a reductions approach to fair classification"
    - "eliminating latent discrimination train then mask"
    - "on fairness and calibration"

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.LG(Machine Learning)