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 campaigns optimization using attribute selection"):
  Abstract: Real-Time Bidding is nowadays one of the most promising systems in the online advertising ecosystem. In the presented study, the performance of RTB campaigns is improved by optimising the parameters of the users' profiles and the publishers' websites. Most studies about optimising RTB campaigns are focused on the bidding strategy. In contrast, the objective of our research consists of optimising RTB campaigns by finding out configurations that maximise both the number of impressions and their average profitability. The experiments demonstrate that, when the number of required visits by advertisers is low, it is easy to find configurations with high average profitability, but as the required number of visits increases, the average profitability tends to go down. Additionally, configuration optimisation has been combined with other interesting strategies to increase, even more, the campaigns' profitability. Along with parameter configuration the study considers the following complementary strategies to increase profitability: i) selecting multiple configurations with a small number of visits instead of a unique configuration with a large number, ii) discarding visits according to the thresholds of cost and profitability, iii) analysing a reduced space of the dataset and extrapolating the solution, and iv) increasing the search space by including solutions below the required number of visits. The developed campaign optimisation methodology could be offered by RTB platforms to advertisers to make their campaigns more profitable.

Includes members:
  Member "managing risk of bidding in display advertising":
    also connected through other hyperedges:
      (none)
  Member "real time bidding by reinforcement learning in display advertising":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "jointly learning to recommend and advertise")
  Member "neural feature embedding for user response prediction in real time bidding rtb":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "display advertising with real time bidding rtb and behavioural targeting")
  Member "display advertising with real time bidding rtb and behavioural targeting":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online joint bid daily budget optimization of internet advertising campaigns")
  Member "smart pacing for effective online ad campaign optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (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-6 (induced by source paper "online joint bid daily budget optimization of internet advertising campaigns")
  Member "sequential selection of correlated ads by pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pricing average price advertising options when underlying spot market prices are discontinuous")

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)