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: "ifair learning individually fair data representations for algorithmic decision making"
  Abstract: People are rated and ranked, towards algorithmic decision making in an increasing number of applications, typically based on machine learning. Research on how to incorporate fairness into such tasks has prevalently pursued the paradigm of group fairness: giving adequate success rates to specifically protected groups. In contrast, the alternative paradigm of individual fairness has received relatively little attention, and this paper advances this less explored direction. The paper introduces a method for probabilistically mapping user records into a low-rank representation that reconciles individual fairness and the utility of classifiers and rankings in downstream applications. Our notion of individual fairness requires that users who are similar in all task-relevant attributes such as job qualification, and disregarding all potentially discriminating attributes such as gender, should have similar outcomes. We demonstrate the versatility of our method by applying it to classification and learning-to-rank tasks on a variety of real-world datasets. Our experiments show substantial improvements over the best prior work for this setting.

Connected through hyperedge HE-1 (induced by source paper "fairness in learning based sequential decision algorithms a survey"):
  with members:
    - "achieving fairness in the stochastic multi armed bandit problem"
    - "fair contextual multi armed bandits theory and experiments"
    - "on the long term impact of algorithmic decision policies effort unfairness and feature segregation through social learning"
    - "the disparate equilibria of algorithmic decision making when individuals invest rationally"
    - "a survey on bias and fairness in machine learning"
    - "fairness with dynamics"
    - "group retention when using machine learning in sequential decision making the interplay between user dynamics and fairness"
    - "combinatorial sleeping bandits with fairness constraints"

Connected through hyperedge HE-2 (induced by source paper "noise tolerant fair classification"):
  with members:
    - "agnostic federated learning"
    - "fairness risk measures"
    - "training well generalizing classifiers for fairness metrics and other data dependent constraints"
    - "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"
    - "differentially private fair learning"
    - "fairness through computationally bounded awareness"
    - "a reductions approach to fair classification"

Connected through hyperedge HE-3 (induced by source paper "operationalizing individual fairness with pairwise fair representations"):
  with members:
    - "eliciting and enforcing subjective individual fairness"
    - "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"
    - "representation learning on graphs methods and applications"
    - "ranking with fairness constraints"
    - "algorithmic decision making and the cost of fairness"

Connected through hyperedge HE-4 (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"
    - "eliciting and enforcing subjective individual fairness"
    - "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices"
    - "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-5 (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"
    - "eliciting and enforcing subjective individual fairness"
    - "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"

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)