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 "long term impact of fair machine learning in sequential decision making representation disparity and group retention"):
  Abstract: Machine learning models trained on data from multiple demographic groups can inherit representation disparity (Hashimoto et al., 2018) that may exist in the data: the group contributing less to the training process may suffer higher loss in model accuracy; this in turn can degrade population retention in these groups over time in terms of their contribution to the training process of future models, which then exacerbates representation disparity in the long run. In this study, we seek to understand the interplay between the model accuracy and the underlying group representation and how they evolve in a sequential decision setting over an infinite horizon, and how the use of fair machine learning plays a role in this process. Using a simple user dynamics (arrival and departure) model, we characterize the long-term property of using machine learning models under a set of fairness criteria imposed on each stage of the decision process, including the commonly used statistical parity and equal opportunity fairness. We show that under this particular arrival/departure model, both these criteria cause the representation disparity to worsen over time, resulting in groups diminishing entirely from the sample pool, while the criterion of equalized loss fares much better. Our results serve to highlight the fact that fairness cannot be defined outside the larger feedback loop where past actions taken by users (who are either subject to the decisions made by the algorithm or whose data are used to train the algorithm or both) will determine future observations and decisions.

Includes members:
  Member "delayed impact of fair 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 "the frontiers of fairness in machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "algorithmic fairness")
  Member "sbeed convergent reinforcement learning with nonlinear function approximation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "provably convergent two timescale off policy actor critic with function approximation")
  Member "inherent trade offs in the fair determination of risk scores":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "algorithmic fairness")
  Member "demographic dialectal variation in social media a case study of african american english":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "theory in theory out how social theory can solve problems that machine learning can t")

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)