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 in supervised learning an information theoretic approach"
  Abstract: Automated decision making systems are increasingly being used in real-world applications. In these systems for the most part, the decision rules are derived by minimizing the training error on the available historical data. Therefore, if there is a bias related to a sensitive attribute such as gender, race, religion, etc. in the data, say, due to cultural/historical discriminatory practices against a certain demographic, the system could continue discrimination in decisions by including the said bias in its decision rule. We present an information theoretic framework for designing fair predictors from data, which aim to prevent discrimination against a specified sensitive attribute in a supervised learning setting. We use equalized odds as the criterion for discrimination, which demands that the prediction should be independent of the protected attribute conditioned on the actual label. To ensure fairness and generalization simultaneously, we compress the data to an auxiliary variable, which is used for the prediction task. This auxiliary variable is chosen such that it is decontaminated from the discriminatory attribute in the sense of equalized odds. The final predictor is obtained by applying a Bayesian decision rule to the auxiliary variable.

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 "fast fair regression via efficient approximations of mutual information"):
  with members:
    - "50 years of test un fairness lessons for machine learning"
    - "fair regression quantitative definitions and reduction based algorithms"
    - "a general framework for fair regression"
    - "a convex framework for fair regression"
    - "estimating mutual information for discrete continuous mixtures"
    - "demystifying fixed k nearest neighbor information estimators"

Connected through hyperedge HE-3 (induced by source paper "fairness measures for regression via probabilistic classification"):
  with members:
    - "50 years of test un fairness lessons for machine learning"
    - "fairness risk measures"
    - "a general framework for fair regression"
    - "a convex framework for fair regression"

Connected through hyperedge HE-4 (induced by source paper "learning generative adversarial representations gap under fairness and censoring constraints"):
  with members:
    - "flexibly fair representation learning by disentanglement"
    - "distributed generation of privacy preserving data with user customization"
    - "privacy under hard distortion constraints"
    - "mitigating unwanted biases with adversarial learning"
    - "learning adversarially fair and transferable representations"
    - "fairgan fairness aware generative adversarial networks"
    - "learning to protect communications with adversarial neural cryptography"
    - "data decisions and theoretical implications when adversarially learning fair representations"

Connected through hyperedge HE-5 (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 moral framework for understanding of fair ml through economic models of equality of opportunity"
    - "a reductions approach to fair classification"
    - "fairness behind a veil of ignorance a welfare analysis for automated decision making"
    - "spectral algorithms for computing fair support vector machines"
    - "equality of opportunity in supervised learning"
    - "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)