human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "on the apparent conflict between individual and group fairness"):
  Abstract: A distinction has been drawn in fair machine learning research between `group' and `individual' fairness measures. Many technical research papers assume that both are important, but conflicting, and propose ways to minimise the trade-offs between these measures. This paper argues that this apparent conflict is based on a misconception. It draws on theoretical discussions from within the fair machine learning research, and from political and legal philosophy, to argue that individual and group fairness are not fundamentally in conflict. First, it outlines accounts of egalitarian fairness which encompass plausible motivations for both group and individual fairness, thereby suggesting that there need be no conflict in principle. Second, it considers the concept of individual justice, from legal philosophy and jurisprudence which seems similar but actually contradicts the notion of individual fairness as proposed in the fair machine learning literature. The conclusion is that the apparent conflict between individual and group fairness is more of an artifact of the blunt application of fairness measures, rather than a matter of conflicting principles. In practice, this conflict may be resolved by a nuanced consideration of the sources of `unfairness' in a particular deployment context, and the carefully justified application of measures to mitigate it.

Includes members:
  Member "50 years of test un fairness lessons for machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "demographic bias in biometrics a survey on an emerging challenge")
  Member "eliciting and enforcing subjective individual fairness":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "individual fairness revisited transferring techniques from adversarial robustness")
  Member "ifair learning individually fair data representations for algorithmic decision making":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fairness in learning based sequential decision algorithms a survey")
  Member "a moral framework for understanding of fair ml through economic models of equality of opportunity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "no computation without representation avoiding data and algorithm biases through diversity")
  Member "fairness and accountability design needs for algorithmic support in high stakes public sector decision making":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "factors influencing perceived fairness in algorithmic decision making algorithm outcomes development procedures and individual differences")
  Member "preventing fairness gerrymandering auditing and learning for subgroup fairness":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "to split or not to split the impact of disparate treatment in classification")
  Member "fairness in machine learning lessons from political philosophy":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "data and model dependencies of membership inference attack")
  Member "calibration for the computationally identifiable masses":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "putting fairness principles into practice challenges metrics and improvements")

Question: Please tell me which class the source paper 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)