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 "real time bidding by reinforcement learning in display advertising"):
  Abstract: The majority of online display ads are served through real-time bidding (RTB) --- each ad display impression is auctioned off in real-time when it is just being generated from a user visit. To place an ad automatically and optimally, it is critical for advertisers to devise a learning algorithm to cleverly bid an ad impression in real-time. Most previous works consider the bid decision as a static optimization problem of either treating the value of each impression independently or setting a bid price to each segment of ad volume. However, the bidding for a given ad campaign would repeatedly happen during its life span before the budget runs out. As such, each bid is strategically correlated by the constrained budget and the overall effectiveness of the campaign (e.g., the rewards from generated clicks), which is only observed after the campaign has completed. Thus, it is of great interest to devise an optimal bidding strategy sequentially so that the campaign budget can be dynamically allocated across all the available impressions on the basis of both the immediate and future rewards. In this paper, we formulate the bid decision process as a reinforcement learning problem, where the state space is represented by the auction information and the campaign's real-time parameters, while an action is the bid price to set. By modeling the state transition via auction competition, we build a Markov Decision Process framework for learning the optimal bidding policy to optimize the advertising performance in the dynamic real-time bidding environment. Furthermore, the scalability problem from the large real-world auction volume and campaign budget is well handled by state value approximation using neural networks. The empirical study on two large-scale real-world datasets and the live A/B testing on a commercial platform have demonstrated the superior performance and high efficiency compared to state-of-the-art methods.

Includes members:
  Member "display advertising with real time bidding rtb and behavioural targeting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online joint bid daily budget optimization of internet advertising campaigns")
  Member "statistical arbitrage mining for display advertising":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bidding machine learning to bid for directly optimizing profits in display advertising")
  Member "smart pacing for effective online ad campaign optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "motiac multi objective actor critics for real time bidding")
  Member "real time bidding for online advertising measurement and analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "motiac multi objective actor critics for real time bidding")
  Member "real time bid optimization with smooth budget delivery in online advertising":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online joint bid daily budget optimization of internet advertising campaigns")
  Member "budget optimization for sponsored search censored learning in mdps":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "achieving fairness in the stochastic multi armed bandit problem")
  Member "sequential selection of correlated ads by pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "real time bidding campaigns optimization using attribute selection")

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)