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: "display advertising with real time bidding rtb and behavioural targeting"
  Abstract: The most significant progress in recent years in online display advertising is what is known as the Real-Time Bidding (RTB) mechanism to buy and sell ads. RTB essentially facilitates buying an individual ad impression in real time while it is still being generated from a user's visit. RTB not only scales up the buying process by aggregating a large amount of available inventories across publishers but, most importantly, enables direct targeting of individual users. As such, RTB has fundamentally changed the landscape of digital marketing. Scientifically, the demand for automation, integration and optimisation in RTB also brings new research opportunities in information retrieval, data mining, machine learning and other related fields. In this monograph, an overview is given of the fundamental infrastructure, algorithms, and technical solutions of this new frontier of computational advertising. The covered topics include user response prediction, bid landscape forecasting, bidding algorithms, revenue optimisation, statistical arbitrage, dynamic pricing, and ad fraud detection.

Connected through hyperedge HE-1 (induced by source paper "online joint bid daily budget optimization of internet advertising campaigns"):
  with members:
    - "bid optimization by multivariable control in display advertising"
    - "budget constrained bidding by model free reinforcement learning in display advertising"
    - "combinatorial semi bandits with knapsacks"
    - "feedback control of real time display advertising"
    - "combinatorial semi bandit with known covariance"
    - "multi touch attribution based budget allocation in online advertising"
    - "thompson sampling for budgeted multi armed bandits"
    - "smart pacing for effective online ad campaign optimization"

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"
    - "adam a method for stochastic optimization"
    - "real time bidding benchmarking with ipinyou dataset"
    - "real time bidding for online advertising measurement and analysis"

Connected through hyperedge HE-3 (induced by source paper "deep personalized re targeting"):
  with members:
    - "feature generation by convolutional neural network for click through rate prediction"
    - "large scale collaborative filtering with product embeddings"
    - "a survey on session based recommender systems"
    - "an attentive survey of attention models"
    - "predicting purchasing intent automatic feature learning using recurrent neural networks"
    - "deep neural network marketplace recommenders in online experiments"
    - "deep neural net with attention for multi channel multi touch attribution"
    - "a hierarchical bayesian model for size recommendation in fashion"

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"
    - "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"
    - "real time bidding for online advertising measurement and analysis"

Connected through hyperedge HE-5 (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"
    - "smart pacing for effective online ad campaign optimization"
    - "real time bidding for online advertising measurement and analysis"
    - "real time bid optimization with smooth budget delivery in online advertising"
    - "sequential selection of correlated ads by pomdps"

Connected through hyperedge HE-6 (induced by source paper "zorro a model agnostic system to price consumer data"):
  with members:
    - "a marketplace for data an algorithmic solution"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "tensorflow a system for large scale machine learning"
    - "mllib machine learning in apache spark"
    - "real time bidding benchmarking with ipinyou dataset"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-7 (induced by source paper "learning to advertise with adaptive exposure via constrained two level reinforcement learning"):
  with members:
    - "learning to collaborate multi scenario ranking via multi agent reinforcement learning"
    - "deep reinforcement learning for sponsored search real time bidding"
    - "reward constrained policy optimization"
    - "reinforcement learning to rank in e commerce search engine formalization analysis and application"
    - "budget constrained bidding by model free reinforcement learning in display advertising"
    - "hindsight experience replay"
    - "reinforcement mechanism design for e commerce"
    - "real time bidding by reinforcement learning in display advertising"

Connected through hyperedge HE-8 (induced by source paper "learning adaptive display exposure for real time advertising"):
  with members:
    - "deep reinforcement learning for sponsored search real time bidding"
    - "reward constrained policy optimization"
    - "reinforcement learning to rank in e commerce search engine formalization analysis and application"
    - "budget constrained bidding by model free reinforcement learning in display advertising"
    - "hindsight experience replay"
    - "reinforcement mechanism design for e commerce"
    - "real time bidding by reinforcement learning in display advertising"
    - "constrained policy optimization"

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)