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 "bayesian exploration with heterogeneous agents"):
  Abstract: It is common in recommendation systems that users both consume and produce information as they make strategic choices under uncertainty. While a social planner would balance "exploration" and "exploitation" using a multi-armed bandit algorithm, users' incentives may tilt this balance in favor of exploitation. We consider Bayesian Exploration: a simple model in which the recommendation system (the "principal") controls the information flow to the users (the "agents") and strives to incentivize exploration via information asymmetry. A single round of this model is a version of a well-known "Bayesian Persuasion game" from [Kamenica and Gentzkow]. We allow heterogeneous users, relaxing a major assumption from prior work that users have the same preferences from one time step to another. The goal is now to learn the best personalized recommendations. One particular challenge is that it may be impossible to incentivize some of the user types to take some of the actions, no matter what the principal does or how much time she has. We consider several versions of the model, depending on whether and when the user types are reported to the principal, and design a near-optimal "recommendation policy" for each version. We also investigate how the model choice and the diversity of user types impact the set of actions that can possibly be "explored" by each type.

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 "overabundant information and learning traps":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "incentivizing exploration with selective data disclosure")
  Member "incentivizing exploration with unbiased histories":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "incentivising exploration and recommendations for contextual bandits with payments")
  Member "competing bandits learning under competition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the gossiping insert eliminate algorithm for multi agent bandits")
  Member "fairness incentives for myopic agents":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "algorithmic and economic perspectives on fairness")
  Member "optimal and myopic information acquisition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "incentivizing exploration with selective data disclosure")
  Member "selling to a no regret buyer":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "grinding the space learning to classify against strategic agents")

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)