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 "budget constrained bidding by model free reinforcement learning in display advertising"):
  Abstract: Real-time bidding (RTB) is an important mechanism in online display advertising, where a proper bid for each page view plays an essential role for good marketing results. Budget constrained bidding is a typical scenario in RTB where the advertisers hope to maximize the total value of the winning impressions under a pre-set budget constraint. However, the optimal bidding strategy is hard to be derived due to the complexity and volatility of the auction environment. To address these challenges, in this paper, we formulate budget constrained bidding as a Markov Decision Process and propose a model-free reinforcement learning framework to resolve the optimization problem. Our analysis shows that the immediate reward from environment is misleading under a critical resource constraint. Therefore, we innovate a reward function design methodology for the reinforcement learning problems with constraints. Based on the new reward design, we employ a deep neural network to learn the appropriate reward so that the optimal policy can be learned effectively. Different from the prior model-based work, which suffers from the scalability problem, our framework is easy to be deployed in large-scale industrial applications. The experimental evaluations demonstrate the effectiveness of our framework on large-scale real datasets.

Includes members:
  Member "multiplicative pacing equilibria in auction markets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stability and learning in strategic queuing systems")
  Member "real time bidding by reinforcement learning in display advertising":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "jointly learning to recommend and advertise")
  Member "optimal real time bidding frameworks discussion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "display advertising with real time bidding rtb and behavioural targeting")
  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.AI(Artificial Intelligence)