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: "optimal real time bidding frameworks discussion"
  Abstract: This note is a complementary material for the solution of optimal real-time bidding function in paper "Optimal Real-Time Bidding for Display Advertising, KDD 2014", where the estimated cost is taken as the bid price, i.e., the upper bound of the true cost. Here we discuss a more general bid optimisation framework with various utility and cost functions.

Connected through hyperedge HE-1 (induced by source paper "budget constrained bidding by model free reinforcement learning in display advertising"):
  with members:
    - "multiplicative pacing equilibria in auction markets"
    - "real time bidding by reinforcement learning in 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"
    - "sequential selection of correlated ads by pomdps"

Connected through hyperedge HE-2 (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"
    - "product based neural networks for user response prediction"
    - "lift based bidding in ad selection"
    - "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)