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 "online joint bid daily budget optimization of internet advertising campaigns"):
  Abstract: Pay-per-click advertising includes various formats (\emph{e.g.}, search, contextual, social) with a total investment of more than 200 billion USD per year worldwide. An advertiser is given a daily budget to allocate over several, even thousands, campaigns, mainly distinguishing for the ad, target, or channel. Furthermore, publishers choose the ads to display and how to allocate them employing auctioning mechanisms, in which every day the advertisers set for each campaign a bid corresponding to the maximum amount of money per click they are willing to pay and the fraction of the daily budget to invest. In this paper, we study the problem of automating the online joint bid/daily budget optimization of pay-per-click advertising campaigns over multiple channels. We formulate our problem as a combinatorial semi-bandit problem, which requires solving a special case of the Multiple-Choice Knapsack problem every day. Furthermore, for every campaign, we capture the dependency of the number of clicks on the bid and daily budget by Gaussian Processes, thus requiring mild assumptions on the regularity of these functions. We design four algorithms and show that they suffer from a regret that is upper bounded with high probability as O(sqrt{T}), where T is the time horizon of the learning process. We experimentally evaluate our algorithms with synthetic settings generated from real data from Yahoo!, and we present the results of the adoption of our algorithms in a real-world application with a daily average spent of 1,000 Euros for more than one year.

Includes members:
  Member "bid optimization by multivariable control in display advertising":
    also connected through other hyperedges:
      (none)
  Member "budget constrained bidding by model free reinforcement learning in display advertising":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "jointly learning to recommend and advertise")
  Member "combinatorial semi bandits with knapsacks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "advances in bandits with knapsacks")
  Member "display advertising with real time bidding rtb and behavioural targeting":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scalable bid landscape forecasting in real time bidding")
  Member "feedback control of real time display advertising":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bid optimization by multivariable control in display advertising")
  Member "combinatorial semi bandit with known covariance":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "tight lower bounds for combinatorial multi armed bandits")
  Member "multi touch attribution based budget allocation in online advertising":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning multi touch conversion attribution with dual attention mechanisms for online advertising")
  Member "thompson sampling for budgeted multi armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "budget constrained bandits over general cost and reward distributions")

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.LG(Machine Learning)