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: "multiaccuracy black box post processing for fairness in classification"
  Abstract: Prediction systems are successfully deployed in applications ranging from disease diagnosis, to predicting credit worthiness, to image recognition. Even when the overall accuracy is high, these systems may exhibit systematic biases that harm specific subpopulations; such biases may arise inadvertently due to underrepresentation in the data used to train a machine-learning model, or as the result of intentional malicious discrimination. We develop a rigorous framework of *multiaccuracy* auditing and post-processing to ensure accurate predictions across *identifiable subgroups*. #R##N#Our algorithm, MULTIACCURACY-BOOST, works in any setting where we have black-box access to a predictor and a relatively small set of labeled data for auditing; importantly, this black-box framework allows for improved fairness and accountability of predictions, even when the predictor is minimally transparent. We prove that MULTIACCURACY-BOOST converges efficiently and show that if the initial model is accurate on an identifiable subgroup, then the post-processed model will be also. We experimentally demonstrate the effectiveness of the approach to improve the accuracy among minority subgroups in diverse applications (image classification, finance, population health). Interestingly, MULTIACCURACY-BOOST can improve subpopulation accuracy (e.g. for "black women") even when the sensitive features (e.g. "race", "gender") are not given to the algorithm explicitly.

Connected through hyperedge HE-1 (induced by source paper "to split or not to split the impact of disparate treatment in classification"):
  with members:
    - "algorithmic fairness"
    - "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"

Connected through hyperedge HE-2 (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-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"
    - "fairness under composition"

Connected through hyperedge HE-4 (induced by source paper "auditing and achieving intersectional fairness in classification problems"):
  with members:
    - "flexibly fair representation learning by disentanglement"
    - "fairvis visual analytics for discovering intersectional bias in machine learning"
    - "automated data slicing for model validation a big data ai integration approach"
    - "mitigating unwanted biases with adversarial learning"
    - "learning adversarially fair and transferable representations"
    - "differentially private fair learning"
    - "an empirical study of rich subgroup fairness for machine learning"
    - "an intersectional definition of fairness"

Connected through hyperedge HE-5 (induced by source paper "advancing subgroup fairness via sleeping experts"):
  with members:
    - "individual fairness in hindsight"
    - "online learning with an unknown fairness metric"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "the externalities of exploration and how data diversity helps exploitation"
    - "an algorithmic framework to control bias in bandit based personalization"
    - "on preserving non discrimination when combining expert advice"
    - "fairness through computationally bounded awareness"
    - "a reductions approach to fair classification"

Connected through hyperedge HE-6 (induced by source paper "who s responsible jointly quantifying the contribution of the learning algorithm and training data"):
  with members:
    - "towards efficient data valuation based on the shapley value"
    - "consistent individualized feature attribution for tree ensembles"
    - "l shapley and c shapley efficient model interpretation for structured data"
    - "a marketplace for data an algorithmic solution"
    - "a unified approach to interpreting model predictions"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deep learning face attributes in the wild"
    - "bounding the estimation error of sampling based shapley value approximation"

Connected through hyperedge HE-7 (induced by source paper "average individual fairness algorithms generalization and experiments"):
  with members:
    - "online learning with an unknown fairness metric"
    - "fairness through computationally bounded awareness"
    - "an empirical study of rich subgroup fairness for machine learning"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "fairness in learning classic and contextual bandits"
    - "equality of opportunity in supervised learning"
    - "inherent trade offs in the fair determination of risk scores"

Connected through hyperedge HE-8 (induced by source paper "the frontiers of fairness in machine learning"):
  with members:
    - "delayed impact of fair machine learning"
    - "downstream effects of affirmative action"
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "a short term intervention for long term fairness in the labor market"
    - "online learning with an unknown fairness metric"
    - "selection problems in the presence of implicit bias"
    - "mitigating unwanted biases with adversarial learning"
    - "learning adversarially fair and transferable representations"

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)