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: "on fairness and calibration"
  Abstract: The machine learning community has become increasingly concerned with the potential for bias and discrimination in predictive models. This has motivated a growing line of work on what it means for a classification procedure to be "fair." In this paper, we investigate the tension between minimizing error disparity across different population groups while maintaining calibrated probability estimates. We show that calibration is compatible only with a single error constraint (i.e. equal false-negatives rates across groups), and show that any algorithm that satisfies this relaxation is no better than randomizing a percentage of predictions for an existing classifier. These unsettling findings, which extend and generalize existing results, are empirically confirmed on several datasets.

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 "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-3 (induced by source paper "fair active learning"):
  with members:
    - "stable and fair classification"
    - "a survey on bias and fairness in machine learning"
    - "a nutritional label for rankings"
    - "achieving fairness through adversarial learning an application to recidivism prediction"
    - "assessing and remedying coverage for a given dataset"
    - "classification with fairness constraints a meta algorithm with provable guarantees"
    - "a reductions approach to fair classification"
    - "active fairness in algorithmic decision making"

Connected through hyperedge HE-4 (induced by source paper "fr train a mutual information based approach to fair and robust training"):
  with members:
    - "identifying and correcting label bias in machine learning"
    - "training well generalizing classifiers for fairness metrics and other data dependent constraints"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "mitigating unwanted biases with adversarial learning"
    - "learning to reweight examples for robust deep learning"
    - "the frontiers of fairness in machine learning"
    - "two player games for efficient non convex constrained optimization"
    - "using trusted data to train deep networks on labels corrupted by severe noise"

Connected through hyperedge HE-5 (induced by source paper "addressing multiple metrics of group fairness in data driven decision making"):
  with members:
    - "racial categories in machine learning"
    - "delayed impact of fair machine learning"
    - "aequitas a bias and fairness audit toolkit"
    - "a reductions approach to fair classification"
    - "a convex framework for fair regression"
    - "fairness testing testing software for discrimination"
    - "algorithmic decision making and the cost of fairness"
    - "equality of opportunity in supervised learning"

Connected through hyperedge HE-6 (induced by source paper "keeping community in the loop understanding wikipedia stakeholder values for machine learning based systems"):
  with members:
    - "the frontiers of fairness in machine learning"
    - "an empirical study of rich subgroup fairness for machine learning"
    - "fairness and accountability design needs for algorithmic support in high stakes public sector decision making"
    - "network dissection quantifying interpretability of deep visual representations"
    - "explanation in artificial intelligence insights from the social sciences"
    - "operationalizing conflict and cooperation between automated software agents in wikipedia a replication and expansion of even good bots fight"
    - "algorithmic decision making and the cost of fairness"
    - "generating visual explanations"

Connected through hyperedge HE-7 (induced by source paper "field aware calibration a simple and empirically strong method for reliable probabilistic predictions"):
  with members:
    - "warm up cold start advertisements improving ctr predictions via learning to learn id embeddings"
    - "large scale study of curiosity driven learning"
    - "policy optimization with model based explorations"
    - "bias reduced uncertainty estimation for deep neural classifiers"
    - "on calibration of modern neural networks"
    - "deep bayesian active learning with image data"
    - "online isotonic regression"
    - "inherent trade offs in the fair determination of risk scores"

Connected through hyperedge HE-8 (induced by source paper "counterfactual fairness removing direct effects through regularization"):
  with members:
    - "a framework for understanding unintended consequences of machine learning"
    - "a neural network framework for fair classifier"
    - "causal reasoning for algorithmic fairness"
    - "mitigating unwanted biases with adversarial learning"
    - "a reductions approach to fair classification"
    - "equality of opportunity in supervised learning"
    - "direct and indirect effects"
    - "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)