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: "a moral framework for understanding of fair ml through economic models of equality of opportunity"
  Abstract: We map the recently proposed notions of algorithmic fairness to economic models of Equality of opportunity (EOP)---an extensively studied ideal of fairness in political philosophy. We formally show that through our conceptual mapping, many existing definition of algorithmic fairness, such as predictive value parity and equality of odds, can be interpreted as special cases of EOP. In this respect, our work serves as a unifying moral framework for understanding existing notions of algorithmic fairness. Most importantly, this framework allows us to explicitly spell out the moral assumptions underlying each notion of fairness, and interpret recent fairness impossibility results in a new light. Last but not least and inspired by luck egalitarian models of EOP, we propose a new family of measures for algorithmic fairness. We illustrate our proposal empirically and show that employing a measure of algorithmic (un)fairness when its underlying moral assumptions are not satisfied, can have devastating consequences for the disadvantaged group's welfare.

Connected through hyperedge HE-1 (induced by source paper "no computation without representation avoiding data and algorithm biases through diversity"):
  with members:
    - "problem formulation and fairness"
    - "efficient search for diverse coherent explanations"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "crowdsourcing with fairness diversity and budget constraints"
    - "the social cost of strategic classification"
    - "fairness through causal awareness learning latent variable models for biased data"
    - "creating fair models of atherosclerotic cardiovascular disease risk"
    - "classification with fairness constraints a meta algorithm with provable guarantees"

Connected through hyperedge HE-2 (induced by source paper "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning"):
  with members:
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices"
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"
    - "how do fairness definitions fare examining public attitudes towards algorithmic definitions of fairness"
    - "manipulating and measuring model interpretability"
    - "fairness and accountability design needs for algorithmic support in high stakes public sector decision making"
    - "a voting based system for ethical decision making"
    - "on formalizing fairness in prediction with machine learning"

Connected through hyperedge HE-3 (induced by source paper "noise tolerant fair classification"):
  with members:
    - "agnostic federated learning"
    - "fairness risk measures"
    - "training well generalizing classifiers for fairness metrics and other data dependent constraints"
    - "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices"
    - "ifair learning individually fair data representations for algorithmic decision making"
    - "differentially private fair learning"
    - "fairness through computationally bounded awareness"
    - "a reductions approach to fair classification"

Connected through hyperedge HE-4 (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"
    - "downstream effects of affirmative action"
    - "the social cost of strategic classification"
    - "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-5 (induced by source paper "on the apparent conflict between individual and group fairness"):
  with members:
    - "50 years of test un fairness lessons for machine learning"
    - "eliciting and enforcing subjective individual fairness"
    - "ifair learning individually fair data representations for algorithmic decision making"
    - "fairness and accountability design needs for algorithmic support in high stakes public sector decision making"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "fairness in machine learning lessons from political philosophy"
    - "calibration for the computationally identifiable masses"
    - "fairness in learning classic and contextual bandits"

Connected through hyperedge HE-6 (induced by source paper "a human in the loop framework to construct context dependent mathematical formulations of fairness"):
  with members:
    - "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"
    - "how do fairness definitions fare examining public attitudes towards algorithmic definitions of fairness"
    - "fairness and accountability design needs for algorithmic support in high stakes public sector decision making"
    - "a voting based system for ethical decision making"
    - "equality of opportunity in supervised learning"
    - "inherent trade offs in the fair determination of risk scores"

Connected through hyperedge HE-7 (induced by source paper "fairness risk measures"):
  with members:
    - "unleashing linear optimizers for group fair learning and optimization"
    - "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices"
    - "a reductions approach to fair classification"
    - "fairness behind a veil of ignorance a welfare analysis for automated decision making"
    - "fairness in supervised learning an information theoretic approach"
    - "spectral algorithms for computing fair support vector machines"
    - "equality of opportunity in supervised learning"
    - "fairness through awareness"

Connected through hyperedge HE-8 (induced by source paper "discriminative but not discriminatory a comparison of fairness definitions under different worldviews"):
  with members:
    - "downstream effects of affirmative action"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "equality of opportunity in supervised learning"
    - "on the im possibility of fairness"
    - "inherent trade offs in the fair determination of risk scores"
    - "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.LG(Machine Learning)