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: "from soft classifiers to hard decisions how fair can we be"
  Abstract: A popular methodology for building binary decision-making classifiers in the presence of imperfect information is to first construct a non-binary "scoring" classifier that is calibrated over all protected groups, and then to post-process this score to obtain a binary decision. We study the feasibility of achieving various fairness properties by post-processing calibrated scores, and then show that deferring post-processors allow for more fairness conditions to hold on the final decision. Specifically, we show: #R##N#1. There does not exist a general way to post-process a calibrated classifier to equalize protected groups' positive or negative predictive value (PPV or NPV). For certain "nice" calibrated classifiers, either PPV or NPV can be equalized when the post-processor uses different thresholds across protected groups, though there exist distributions of calibrated scores for which the two measures cannot be both equalized. When the post-processing consists of a single global threshold across all groups, natural fairness properties, such as equalizing PPV in a nontrivial way, do not hold even for "nice" classifiers. #R##N#2. When the post-processing is allowed to `defer' on some decisions (that is, to avoid making a decision by handing off some examples to a separate process), then for the non-deferred decisions, the resulting classifier can be made to equalize PPV, NPV, false positive rate (FPR) and false negative rate (FNR) across the protected groups. This suggests a way to partially evade the impossibility results of Chouldechova and Kleinberg et al., which preclude equalizing all of these measures simultaneously. We also present different deferring strategies and show how they affect the fairness properties of the overall system. #R##N#We evaluate our post-processing techniques using the COMPAS data set from 2016.

Connected through hyperedge HE-1 (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"
    - "a gradient based split criterion for highly accurate and transparent model trees"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "fair clustering through fairlets"
    - "methods for interpreting and understanding deep neural networks"
    - "the price of fair pca one extra dimension"

Connected through hyperedge HE-2 (induced by source paper "individual fairness revisited transferring techniques from adversarial robustness"):
  with members:
    - "metric learning for individual fairness"
    - "eliciting and enforcing subjective individual fairness"
    - "learning fair representations for kernel models"
    - "online learning with an unknown fairness metric"
    - "learning adversarially fair and transferable representations"
    - "the frontiers of fairness in machine learning"
    - "bias mitigation post processing for individual and group fairness"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-3 (induced by source paper "repairing without retraining avoiding disparate impact with counterfactual distributions"):
  with members:
    - "algorithmic fairness"
    - "on the direction of discrimination an information theoretic analysis of disparate impact in machine learning"
    - "a reductions approach to fair classification"
    - "on fairness and calibration"
    - "fairness testing testing software for discrimination"
    - "algorithmic decision making and the cost of fairness"
    - "proxy non discrimination in data driven systems"
    - "equality of opportunity in supervised learning"

Connected through hyperedge HE-4 (induced by source paper "noise induces loss discrepancy across groups for linear regression"):
  with members:
    - "maximum weighted loss discrepancy"
    - "delayed impact of fair machine learning"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "fairness through causal awareness learning latent variable models for biased data"
    - "causal reasoning for algorithmic fairness"
    - "the frontiers of fairness in machine learning"
    - "a reductions approach to fair classification"
    - "penalizing unfairness in binary classification"

Connected through hyperedge HE-5 (induced by source paper "bias mitigation post processing for individual and group fairness"):
  with members:
    - "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "automated test generation to detect individual discrimination in ai models"
    - "on fairness and calibration"
    - "fairness testing testing software for discrimination"
    - "on the safety of machine learning cyber physical systems decision sciences and data products"
    - "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)