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 "achieving fairness in the stochastic multi armed bandit problem"):
  Abstract: We study an interesting variant of the stochastic multi-armed bandit problem, called the Fair-SMAB problem, where each arm is required to be pulled for at least a given fraction of the total available rounds. We investigate the interplay between learning and fairness in terms of a pre-specified vector denoting the fractions of guaranteed pulls. We define a fairness-aware regret, called $r$-Regret, that takes into account the above fairness constraints and naturally extends the conventional notion of regret. Our primary contribution is characterizing a class of Fair-SMAB algorithms by two parameters: the unfairness tolerance and the learning algorithm used as a black-box. We provide a fairness guarantee for this class that holds uniformly over time irrespective of the choice of the learning algorithm. In particular, when the learning algorithm is UCB1, we show that our algorithm achieves $O(\ln T)$ $r$-Regret. Finally, we evaluate the cost of fairness in terms of the conventional notion of regret.

Includes members:
  Member "advancing subgroup fairness via sleeping experts":
    also connected through other hyperedges:
      (none)
  Member "fair logistic regression an adversarial perspective":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "r enyi fair inference")
  Member "fairness in recommendation ranking through pairwise comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the fairness of randomized trials for recommendation with heterogeneous demographics and beyond")
  Member "combinatorial sleeping bandits with fairness constraints":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fairness in learning based sequential decision algorithms a survey")
  Member "policy learning for fairness in ranking":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the fairness of randomized trials for recommendation with heterogeneous demographics and beyond")
  Member "equal opportunity in online classification with partial feedback":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "grinding the space learning to classify against strategic agents")
  Member "introduction to multi armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "effective diversity in population based reinforcement learning")
  Member "online learning with an unknown fairness metric":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fairness in learning based sequential decision algorithms a survey")

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)