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: "eliciting and enforcing subjective individual fairness"
  Abstract: We revisit the notion of individual fairness first proposed by Dwork et al. [2012], which asks that "similar individuals should be treated similarly". A primary difficulty with this definition is that it assumes a completely specified fairness metric for the task at hand. In contrast, we consider a framework for fairness elicitation, in which fairness is indirectly specified only via a sample of pairs of individuals who should be treated (approximately) equally on the task. We make no assumption that these pairs are consistent with any metric. We provide a provably convergent oracle-efficient algorithm for minimizing error subject to the fairness constraints, and prove generalization theorems for both accuracy and fairness. Since the constrained pairs could be elicited either from a panel of judges, or from particular individuals, our framework provides a means for algorithmically enforcing subjective notions of fairness. We report on preliminary findings of a behavioral study of subjective fairness using human-subject fairness constraints elicited on the COMPAS criminal recidivism dataset.

Connected through hyperedge HE-1 (induced by source paper "individual fairness revisited transferring techniques from adversarial robustness"):
  with members:
    - "metric learning for individual fairness"
    - "learning fair representations for kernel models"
    - "from soft classifiers to hard decisions how fair can we be"
    - "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-2 (induced by source paper "metric free individual fairness in online learning"):
  with members:
    - "online non convex learning following the perturbed leader is optimal"
    - "individual fairness in hindsight"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "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-3 (induced by source paper "algorithmic fairness from a non ideal perspective"):
  with members:
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "operationalizing individual fairness with pairwise fair representations"):
  with members:
    - "equity of attention amortizing individual fairness in rankings"
    - "online learning with an unknown fairness metric"
    - "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices"
    - "fair clustering through fairlets"
    - "ifair learning individually fair data representations for algorithmic decision making"
    - "representation learning on graphs methods and applications"
    - "ranking with fairness constraints"
    - "algorithmic decision making and the cost of fairness"

Connected through hyperedge HE-5 (induced by source paper "an empirical study on learning fairness metrics for compas data with human supervision"):
  with members:
    - "operationalizing individual fairness with pairwise fair representations"
    - "metric learning for individual fairness"
    - "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices"
    - "ifair learning individually fair data representations for algorithmic decision making"
    - "calibrated fairness in bandits"
    - "equality of opportunity in supervised learning"
    - "deep metric learning using triplet network"
    - "a survey on metric learning for feature vectors and structured data"

Connected through hyperedge HE-6 (induced by source paper "on the apparent conflict between individual and group fairness"):
  with members:
    - "50 years of test un fairness lessons for machine learning"
    - "ifair learning individually fair data representations for algorithmic decision making"
    - "a moral framework for understanding of fair ml through economic models of equality of opportunity"
    - "fairness and accountability design needs for algorithmic support in high stakes public sector decision making"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "fairness in machine learning lessons from political philosophy"
    - "calibration for the computationally identifiable masses"
    - "fairness in learning classic and contextual bandits"

Connected through hyperedge HE-7 (induced by source paper "preference informed fairness"):
  with members:
    - "discrimination through optimization how facebook s ad delivery can lead to biased outcomes"
    - "individual fairness in sponsored search auctions"
    - "envy free classification"
    - "online learning with an unknown fairness metric"
    - "fairness through computationally bounded awareness"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "calibration for the computationally identifiable masses"
    - "discrimination in online ad delivery"

Connected through hyperedge HE-8 (induced by source paper "on the legal compatibility of fairness definitions"):
  with members:
    - "interpreting social respect a normative lens for ml models"
    - "downstream effects of affirmative action"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "differentially private fair learning"
    - "equality of opportunity in supervised learning"
    - "on the im possibility of fairness"
    - "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)