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: "real time bidding for online advertising measurement and analysis"
  Abstract: The real-time bidding (RTB), aka programmatic buying, has recently become the fastest growing area in online advertising. Instead of bulking buying and inventory-centric buying, RTB mimics stock exchanges and utilises computer algorithms to automatically buy and sell ads in real-time; It uses per impression context and targets the ads to specific people based on data about them, and hence dramatically increases the effectiveness of display advertising. In this paper, we provide an empirical analysis and measurement of a production ad exchange. Using the data sampled from both demand and supply side, we aim to provide first-hand insights into the emerging new impression selling infrastructure and its bidding behaviours, and help identifying research and design issues in such systems. From our study, we observed that periodic patterns occur in various statistics including impressions, clicks, bids, and conversion rates (both post-view and post-click), which suggest time-dependent models would be appropriate for capturing the repeated patterns in RTB. We also found that despite the claimed second price auction, the first price payment in fact is accounted for 55.4% of total cost due to the arrangement of the soft floor price. As such, we argue that the setting of soft floor price in the current RTB systems puts advertisers in a less favourable position. Furthermore, our analysis on the conversation rates shows that the current bidding strategy is far less optimal, indicating the significant needs for optimisation algorithms incorporating the facts such as the temporal behaviours, the frequency and recency of the ad displays, which have not been well considered in the past.

Connected through hyperedge HE-1 (induced by source paper "motiac multi objective actor critics for real time bidding"):
  with members:
    - "a generalized algorithm for multi objective reinforcement learning and policy adaptation"
    - "deep reinforcement learning for sponsored search real time bidding"
    - "dynamic weights in multi objective deep reinforcement learning"
    - "a nonparametric delayed feedback model for conversion rate prediction"
    - "budget constrained bidding by model free reinforcement learning in display advertising"
    - "multi reward reinforced summarization with saliency and entailment"
    - "optimized cost per click in taobao display advertising"
    - "hybrid reward architecture for reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "scalable bid landscape forecasting in real time bidding"):
  with members:
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "machine learning for survival analysis a survey"
    - "deep cross network for ad click predictions"
    - "display advertising with real time bidding rtb and behavioural targeting"
    - "adam a method for stochastic optimization"
    - "real time bidding benchmarking with ipinyou dataset"

Connected through hyperedge HE-3 (induced by source paper "online inference for advertising auctions"):
  with members:
    - "a multi agent reinforcement learning method for impression allocation in online display advertising"
    - "real time bidding by reinforcement learning in display advertising"
    - "a tutorial on thompson sampling"
    - "ladder a human level bidding agent for large scale real time online auctions"
    - "a b testing of auctions"
    - "data efficient off policy policy evaluation for reinforcement learning"
    - "doubly robust off policy value evaluation for reinforcement learning"
    - "counterfactual risk minimization learning from logged bandit feedback"

Connected through hyperedge HE-4 (induced by source paper "deep landscape forecasting for real time bidding advertising"):
  with members:
    - "bidding machine learning to bid for directly optimizing profits in display advertising"
    - "sliced recurrent neural networks"
    - "deep learning for patient specific kidney graft survival analysis"
    - "display advertising with real time bidding rtb and behavioural targeting"
    - "product based neural networks for user response prediction"
    - "architectural complexity measures of recurrent neural networks"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "real time bidding benchmarking with ipinyou dataset"

Connected through hyperedge HE-5 (induced by source paper "bid optimization by multivariable control in display advertising"):
  with members:
    - "budget constrained bidding by model free reinforcement learning in display advertising"
    - "bidding machine learning to bid for directly optimizing profits in display advertising"
    - "real time bidding by reinforcement learning in display advertising"
    - "feedback control of real time display advertising"
    - "smart pacing for effective online ad campaign optimization"
    - "real time bid optimization with smooth budget delivery in online advertising"
    - "a dynamic near optimal algorithm for online linear programming"

Connected through hyperedge HE-6 (induced by source paper "real time bidding campaigns optimization using attribute selection"):
  with members:
    - "managing risk of bidding in display advertising"
    - "real time bidding by reinforcement learning in display advertising"
    - "neural feature embedding for user response prediction in real time bidding rtb"
    - "display advertising with real time bidding rtb and behavioural targeting"
    - "smart pacing for effective online ad campaign optimization"
    - "real time bid optimization with smooth budget delivery in online advertising"
    - "sequential selection of correlated ads by pomdps"

Connected through hyperedge HE-7 (induced by source paper "pricing average price advertising options when underlying spot market prices are discontinuous"):
  with members:
    - "risk aware dynamic reserve prices of programmatic guarantee in display advertising"
    - "a dynamic pricing model for unifying programmatic guarantee and real time bidding in display advertising"
    - "a lattice framework for pricing display advertisement options with the stochastic volatility underlying model"
    - "multi keyword multi click advertisement option contracts for sponsored search"
    - "shale an efficient algorithm for allocation of guaranteed display advertising"
    - "sequential selection of correlated ads by pomdps"

Connected through hyperedge HE-8 (induced by source paper "communication efficient distributed online learning with kernels"):
  with members:
    - "optimal distributed online prediction using mini batches"

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)