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: "lift based bidding in ad selection"
  Abstract: Real-time bidding (RTB) has become one of the largest online advertising markets in the world. Today the bid price per ad impression is typically decided by the expected value of how it can lead to a desired action event (e.g., registering an account or placing a purchase order) to the advertiser. However, this industry standard approach to decide the bid price does not consider the actual effect of the ad shown to the user, which should be measured based on the performance lift among users who have been or have not been exposed to a certain treatment of ads. In this paper, we propose a new bidding strategy and prove that if the bid price is decided based on the performance lift rather than absolute performance value, advertisers can actually gain more action events. We describe the modeling methodology to predict the performance lift and demonstrate the actual performance gain through blind A/B test with real ad campaigns in an industry-leading Demand-Side Platform (DSP). We also discuss the relationship between attribution models and bidding strategies. We prove that, to move the DSPs to bid based on performance lift, they should be rewarded according to the relative performance lift they contribute.

Connected through hyperedge HE-1 (induced by source paper "learning multi touch conversion attribution with dual attention mechanisms for online advertising"):
  with members:
    - "bidding machine learning to bid for directly optimizing profits in display advertising"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "convolutional sequence to sequence learning"
    - "deep learning over multi field categorical data a case study on user response prediction"
    - "display advertising with real time bidding rtb and behavioural targeting"
    - "multi touch attribution based budget allocation in online advertising"
    - "sequential click prediction for sponsored search with recurrent neural networks"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-2 (induced by source paper "bidding machine learning to bid for directly optimizing profits in display advertising"):
  with members:
    - "real time bidding by reinforcement learning in display advertising"
    - "display advertising with real time bidding rtb and behavioural targeting"
    - "product based neural networks for user response prediction"
    - "statistical arbitrage mining for display advertising"
    - "real time bidding for online advertising measurement and analysis"
    - "real time bid optimization with smooth budget delivery in online advertising"
    - "budget optimization for sponsored search censored learning in mdps"

Connected through hyperedge HE-3 (induced by source paper "display advertising with real time bidding rtb and behavioural targeting"):
  with members:
    - "neural feature embedding for user response prediction in real time bidding rtb"
    - "optimal real time bidding frameworks discussion"
    - "product based neural networks for user response prediction"
    - "statistical arbitrage mining for display advertising"
    - "an empirical study on display ad impression viewability measurements"
    - "real time bidding benchmarking with ipinyou dataset"
    - "neural machine translation by jointly learning to align and translate"
    - "real time bid optimization with smooth budget delivery in online advertising"

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)