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 "combinatorial sleeping bandits with fairness constraints"):
  Abstract: The multi-armed bandit (MAB) model has been widely adopted for studying many practical optimization problems (network resource allocation, ad placement, crowdsourcing, etc.) with unknown parameters. The goal of the player here is to maximize the cumulative reward in the face of uncertainty. However, the basic MAB model neglects several important factors of the system in many real-world applications, where multiple arms can be simultaneously played and an arm could sometimes be "sleeping". Besides, ensuring fairness is also a key design concern in practice. To that end, we propose a new Combinatorial Sleeping MAB model with Fairness constraints, called CSMAB-F, aiming to address the aforementioned crucial modeling issues. The objective is now to maximize the reward while satisfying the fairness requirement of a minimum selection fraction for each individual arm. To tackle this new problem, we extend an online learning algorithm, UCB, to deal with a critical tradeoff between exploitation and exploration and employ the virtual queue technique to properly handle the fairness constraints. By carefully integrating these two techniques, we develop a new algorithm, called Learning with Fairness Guarantee (LFG), for the CSMAB-F problem. Further, we rigorously prove that not only LFG is feasibility-optimal, but it also has a time-average regret upper bounded by $\frac{N}{2\eta}+\frac{\beta_1\sqrt{mNT\log{T}}+\beta_2 N}{T}$, where N is the total number of arms, m is the maximum number of arms that can be simultaneously played, T is the time horizon, $\beta_1$ and $\beta_2$ are constants, and $\eta$ is a design parameter that we can tune. Finally, we perform extensive simulations to corroborate the effectiveness of the proposed algorithm. Interestingly, the simulation results reveal an important tradeoff between the regret and the speed of convergence to a point satisfying the fairness constraints.

Includes members:
  Member "achieving fairness in the stochastic multi armed bandit problem":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fairness in learning based sequential decision algorithms a survey")
  Member "beyond the click through rate web link selection with multi level feedback":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "constrained thompson sampling for wireless link optimization")
  Member "fair task allocation in crowdsourced delivery":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "task recommendation in crowdsourcing based on learning preferences and reliabilities")
  Member "fairness in learning classic and contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "algorithmic fairness")
  Member "fair algorithms for infinite and contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "measurement and fairness")
  Member "rawlsian fairness for machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the fairness of randomized trials for recommendation with heterogeneous demographics and beyond")
  Member "combinatorial bandits revisited":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "matroid bandits fast combinatorial optimization with learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "combinatorial semi bandit in the non stationary environment")

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)