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 "sample complexity of incentivized exploration"):
  Abstract: We consider incentivized exploration: a version of multi-armed bandits where the choice of actions is controlled by self-interested agents, and the algorithm can only issue recommendations. The algorithm controls the flow of information, and the information asymmetry can incentivize the agents to explore. Prior work matches the optimal regret rates for bandits up to "constant" multiplicative factors determined by the Bayesian prior. However, the dependence on the prior in prior work could be arbitrarily large, and the dependence on the number of arms K could be exponential. The optimal dependence on the prior and K is very unclear. We make progress on these issues. Our first result is that Thompson sampling is incentive-compatible if initialized with enough data points. Thus, we reduce the problem of designing incentive-compatible algorithms to that of sample complexity: #R##N#(i) How many data points are needed to incentivize Thompson sampling? #R##N#(ii) How many rounds does it take to collect these samples? #R##N#We address both questions, providing upper bounds on sample complexity that are typically polynomial in K and lower bounds that are polynomially matching.

Includes members:
  Member "introduction to multi armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "effective diversity in population based reinforcement learning")
  Member "overabundant information and learning traps":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "incentivizing exploration with selective data disclosure")
  Member "optimal and myopic information acquisition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "incentivizing exploration with selective data disclosure")
  Member "descending price optimally coordinates search":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "incentivizing exploration with selective data disclosure")
  Member "bayesian incentive compatible bandit exploration":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "incentivizing exploration with selective data disclosure")
  Member "economic recommendation systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "incentivizing exploration with selective data disclosure")
  Member "adaptive contract design for crowdsourcing markets bandit algorithms for repeated principal agent problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the complexity of contracts")
  Member "bandits with knapsacks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "exploration exploitation in constrained mdps")

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.GT(Computer Science and Game Theory)