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 "diversity and exploration in social learning"):
  Abstract: In consumer search, there is a set of items. An agent has a prior over her value for each item and can pay a cost to learn the instantiation of her value. After exploring a subset of items, the agent chooses one and obtains a payoff equal to its value minus the search cost. We consider a sequential model of consumer search in which agents' values are correlated and each agent updates her priors based on the exploration of past agents before performing her search. Specifically, we assume the value is the sum of a common-value component, called the quality, and a subjective score. Fixing the variance of the total value, we say a population is more diverse if the subjective score has a larger variance. We ask how diversity impacts average utility. We show that intermediate diversity levels yield significantly higher social utility than the extreme cases of no diversity (when agents under-explore) or full diversity (when agents are unable to learn from each other) and quantify how the impact of the diversity level changes depending on the time spent searching.

Includes members:
  Member "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "algorithmic fairness")
  Member "the externalities of exploration and how data diversity helps exploitation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "incentivizing exploration with selective data disclosure")
  Member "diversity of preferences can increase collective welfare in sequential exploration problems":
    also connected through other hyperedges:
      (none)
  Member "bayesian exploration incentivizing exploration in bayesian games":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "public bayesian persuasion being almost optimal and almost persuasive")
  Member "bayesian incentive compatible bandit exploration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "incentivizing exploration with selective data disclosure")
  Member "incentivizing exploration with heterogeneous value of money":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "incentivising exploration and recommendations for contextual bandits with payments")

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)