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 incentivizing exploration in bayesian games"
  Abstract: We consider a ubiquitous scenario in the Internet economy when individual decision-makers (henceforth, agents) both produce and consume information as they make strategic choices in an uncertain environment. This creates a three-way tradeoff between exploration (trying out insufficiently explored alternatives to help others in the future), exploitation (making optimal decisions given the information discovered by other agents), and incentives of the agents (who are myopically interested in exploitation, while preferring the others to explore). We posit a principal who controls the flow of information from agents that came before, and strives to coordinate the agents towards a socially optimal balance between exploration and exploitation, not using any monetary transfers. The goal is to design a recommendation policy for the principal which respects agents' incentives and minimizes a suitable notion of regret. #R##N#We extend prior work in this direction to allow the agents to interact with one another in a shared environment: at each time step, multiple agents arrive to play a Bayesian game, receive recommendations, choose their actions, receive their payoffs, and then leave the game forever. The agents now face two sources of uncertainty: the actions of the other agents and the parameters of the uncertain game environment. #R##N#Our main contribution is to show that the principal can achieve constant regret when the utilities are deterministic (where the constant depends on the prior distribution, but not on the time horizon), and logarithmic regret when the utilities are stochastic. As a key technical tool, we introduce the concept of explorable actions, the actions which some incentive-compatible policy can recommend with non-zero probability. We show how the principal can identify (and explore) all explorable actions, and use the revealed information to perform optimally.

Connected through hyperedge HE-1 (induced by source paper "public bayesian persuasion being almost optimal and almost persuasive"):
  with members:
    - "persuading voters it s easy to whisper it s hard to speak loud"
    - "on the tractability of public persuasion with no externalities"
    - "targeting and signaling in ad auctions"
    - "algorithmic persuasion with no externalities"
    - "computational aspects of private bayesian persuasion"
    - "inapproximability results for approximate nash equilibria"
    - "can almost everybody be almost happy pcp for ppad and the inapproximability of nash"
    - "algorithmic bayesian persuasion"

Connected through hyperedge HE-2 (induced by source paper "incentivising exploration and recommendations for contextual bandits with payments"):
  with members:
    - "bayesian exploration with heterogeneous agents"
    - "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"
    - "incentivizing exploration with heterogeneous value of money"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-3 (induced by source paper "bayesian exploration with heterogeneous agents"):
  with members:
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "the externalities of exploration and how data diversity helps exploitation"
    - "overabundant information and learning traps"
    - "incentivizing exploration with unbiased histories"
    - "competing bandits learning under competition"
    - "fairness incentives for myopic agents"
    - "optimal and myopic information acquisition"
    - "selling to a no regret buyer"

Connected through hyperedge HE-4 (induced by source paper "on the tractability of public persuasion with no externalities"):
  with members:
    - "algorithmic persuasion with no externalities"
    - "no regret learning in bayesian games"
    - "algorithmic bayesian persuasion"
    - "deterministic algorithms for submodular maximization problems"
    - "hardness results for signaling in bayesian zero sum and network routing games"
    - "honest signaling in zero sum games is hard and lying is even harder"
    - "mixture selection mechanism design and signaling"
    - "tight bounds on ell_1 approximation and learning of self bounding functions"

Connected through hyperedge HE-5 (induced by source paper "algorithmic price discrimination"):
  with members:
    - "settling the sample complexity of single parameter revenue maximization"
    - "learning optimal reserve price against non myopic bidders"
    - "learning multi item auctions with or without samples"
    - "learning simple auctions"
    - "efficient empirical revenue maximization in single parameter auction environments"
    - "sample complexity for non truthful mechanisms"
    - "the sample complexity of revenue maximization"
    - "bayesian incentive compatible bandit exploration"

Connected through hyperedge HE-6 (induced by source paper "optimal algorithm for bayesian incentive compatible exploration"):
  with members:
    - "competing bandits learning under competition"
    - "algorithmic persuasion with no externalities"
    - "persuasion with limited communication"
    - "bayesian incentive compatible bandit exploration"
    - "algorithmic bayesian persuasion"

Connected through hyperedge HE-7 (induced by source paper "diversity and exploration in social learning"):
  with members:
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "the externalities of exploration and how data diversity helps exploitation"
    - "diversity of preferences can increase collective welfare in sequential exploration problems"
    - "bayesian incentive compatible bandit exploration"
    - "incentivizing exploration with heterogeneous value of money"

Connected through hyperedge HE-8 (induced by source paper "strategic inference with a single private sample"):
  with members:
    - "a unified approach to dynamic decision problems with asymmetric information part i non strategic agents"
    - "generalized colonel blotto game"

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)