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: "the measure and mismeasure of fairness a critical review of fair machine learning"
  Abstract: The nascent field of fair machine learning aims to ensure that decisions guided by algorithms are equitable. Over the last several years, three formal definitions of fairness have gained prominence: (1) anti-classification, meaning that protected attributes---like race, gender, and their proxies---are not explicitly used to make decisions; (2) classification parity, meaning that common measures of predictive performance (e.g., false positive and false negative rates) are equal across groups defined by the protected attributes; and (3) calibration, meaning that conditional on risk estimates, outcomes are independent of protected attributes. Here we show that all three of these fairness definitions suffer from significant statistical limitations. Requiring anti-classification or classification parity can, perversely, harm the very groups they were designed to protect; and calibration, though generally desirable, provides little guarantee that decisions are equitable. In contrast to these formal fairness criteria, we argue that it is often preferable to treat similarly risky people similarly, based on the most statistically accurate estimates of risk that one can produce. Such a strategy, while not universally applicable, often aligns well with policy objectives; notably, this strategy will typically violate both anti-classification and classification parity. In practice, it requires significant effort to construct suitable risk estimates. One must carefully define and measure the targets of prediction to avoid retrenching biases in the data. But, importantly, one cannot generally address these difficulties by requiring that algorithms satisfy popular mathematical formalizations of fairness. By highlighting these challenges in the foundation of fair machine learning, we hope to help researchers and practitioners productively advance the area.

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 "bias in data driven ai systems an introductory survey"):
  with members:
    - "50 years of test un fairness lessons for machine learning"
    - "explaining explanations in ai"
    - "adafair cumulative fairness adaptive boosting"
    - "from soft classifiers to hard decisions how fair can we be"
    - "a gradient based split criterion for highly accurate and transparent model trees"
    - "fair clustering through fairlets"
    - "methods for interpreting and understanding deep neural networks"
    - "the price of fair pca one extra dimension"

Connected through hyperedge HE-3 (induced by source paper "balancing competing objectives with noisy data score based classifiers for welfare aware machine learning"):
  with members:
    - "a longitudinal analysis of youtube s promotion of conspiracy videos"
    - "the disparate equilibria of algorithmic decision making when individuals invest rationally"
    - "fairness and utilization in allocating resources with uncertain demand"
    - "fair classification and social welfare"
    - "noise tolerant fair classification"
    - "delayed impact of fair machine learning"
    - "welfare and distributional impacts of fair classification"
    - "a flexible framework for multi objective bayesian optimization using random scalarizations"

Connected through hyperedge HE-4 (induced by source paper "pots protective optimization technologies"):
  with members:
    - "discrimination through optimization how facebook s ad delivery can lead to biased outcomes"
    - "adversarial attacks on copyright detection systems"
    - "explaining explanations in ai"
    - "delayed impact of fair machine learning"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "poisoning attacks to graph based recommender systems"
    - "fighting offensive language on social media with unsupervised text style transfer"
    - "questioning the assumptions behind fairness solutions"

Connected through hyperedge HE-5 (induced by source paper "deontological ethics by monotonicity shape constraints"):
  with members:
    - "optimization with non differentiable constraints with applications to fairness recall churn and other goals"
    - "fairness of exposure in rankings"
    - "two player games for efficient non convex constrained optimization"
    - "a reductions approach to fair classification"
    - "a convex framework for fair regression"
    - "ranking with fairness constraints"
    - "fairness in machine learning lessons from political philosophy"
    - "fa ir a fair top k ranking algorithm"

Connected through hyperedge HE-6 (induced by source paper "metric free individual fairness in online learning"):
  with members:
    - "online non convex learning following the perturbed leader is optimal"
    - "eliciting and enforcing subjective individual fairness"
    - "individual fairness in hindsight"
    - "the frontiers of fairness in machine learning"
    - "a reductions approach to fair classification"
    - "inherent trade offs in the fair determination of risk scores"
    - "efficient algorithms for adversarial contextual learning"

Connected through hyperedge HE-7 (induced by source paper "getting fairness right towards a toolbox for practitioners"):
  with members:
    - "identifying and correcting label bias in machine learning"
    - "equality of opportunity in supervised learning"
    - "semantics derived automatically from language corpora necessarily contain human biases"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "discrimination in online ad delivery"
    - "fairness through awareness"

Connected through hyperedge HE-8 (induced by source paper "algorithmic fairness from a non ideal perspective"):
  with members:
    - "eliciting and enforcing subjective individual fairness"
    - "fairness in machine learning lessons from political philosophy"
    - "equality of opportunity in supervised learning"
    - "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.CY(Computers and Society)