human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "incentivising exploration and recommendations for contextual bandits with payments"):
  with members:
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "multi armed bandits with compensation"
    - "optimal algorithm for bayesian incentive compatible"
    - "incentivizing exploration with unbiased histories"
    - "bayesian exploration incentivizing exploration in bayesian games"
    - "incentivizing exploration with heterogeneous value of money"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-2 (induced by source paper "incentivized exploration for multi armed bandits under reward drift"):
  with members:
    - "the intrinsic robustness of stochastic bandits to strategic manipulation"
    - "multi armed bandits with compensation"
    - "stochastic bandits robust to adversarial corruptions"
    - "incentivizing exploration with unbiased histories"
    - "learning user preferences to incentivize exploration in the sharing economy"
    - "incentivizing exploration with heterogeneous value of money"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "further optimal regret bounds for thompson sampling"

Connected through hyperedge HE-3 (induced by source paper "the intrinsic robustness of stochastic bandits to strategic manipulation"):
  with members:
    - "online learning for measuring incentive compatibility in ad auctions"
    - "stochastic bandits robust to adversarial corruptions"
    - "learning to bid without knowing your value"
    - "multi armed bandit problems with strategic arms"
    - "bayesian incentive compatible bandit exploration"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-4 (induced by source paper "fiduciary bandits"):
  with members:
    - "optimal algorithm for bayesian incentive compatible"
    - "fairness in learning classic and contextual bandits"
    - "equality of opportunity in supervised learning"
    - "bayesian incentive compatible bandit exploration"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "fairness through awareness"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "contextual bandits with similarity information"

Question: Please tell me which class the center node 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)