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 "advancing subgroup fairness via sleeping experts"):
  Abstract: We study methods for improving fairness to subgroups in settings with overlapping populations and sequential predictions. Classical notions of fairness focus on the balance of some property across different populations. However, in many applications the goal of the different groups is not to be predicted equally but rather to be predicted well. We demonstrate that the task of satisfying this guarantee for multiple overlapping groups is not straightforward and show that for the simple objective of unweighted average of false negative and false positive rate, satisfying this for overlapping populations can be statistically impossible even when we are provided predictors that perform well separately on each subgroup. On the positive side, we show that when individuals are equally important to the different groups they belong to, this goal is achievable; to do so, we draw a connection to the sleeping experts literature in online learning. Motivated by the one-sided feedback in natural settings of interest, we extend our results to such a feedback model. We also provide a game-theoretic interpretation of our results, examining the incentives of participants to join the system and to provide the system full information about predictors they may possess. We end with several interesting open problems concerning the strength of guarantees that can be achieved in a computationally efficient manner.

Includes members:
  Member "individual fairness in hindsight":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fairness in learning based sequential decision algorithms a survey")
  Member "multiaccuracy black box post processing for fairness in classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "to split or not to split the impact of disparate treatment in classification")
  Member "online learning with an unknown fairness metric":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fairness in learning based sequential decision algorithms a survey")
  Member "the measure and mismeasure of fairness a critical review of fair machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "algorithmic fairness")
  Member "the externalities of exploration and how data diversity helps exploitation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "incentivizing exploration with selective data disclosure")
  Member "an algorithmic framework to control bias in bandit based personalization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "achieving fairness in the stochastic multi armed bandit problem")
  Member "on preserving non discrimination when combining expert advice":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning fair classifiers in online stochastic settings")
  Member "fairness through computationally bounded awareness":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "personalization of deep learning")

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)