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 bid optimization with smooth budget delivery in online advertising"
  Abstract: Today, billions of display ad impressions are purchased on a daily basis through a public auction hosted by real time bidding (RTB) exchanges. A decision has to be made for advertisers to submit a bid for each selected RTB ad request in milliseconds. Restricted by the budget, the goal is to buy a set of ad impressions to reach as many targeted users as possible. A desired action (conversion), advertiser specific, includes purchasing a product, filling out a form, signing up for emails, etc. In addition, advertisers typically prefer to spend their budget smoothly over the time in order to reach a wider range of audience accessible throughout a day and have a sustainable impact. However, since the conversions occur rarely and the occurrence feedback is normally delayed, it is very challenging to achieve both budget and performance goals at the same time. In this paper, we present an online approach to the smooth budget delivery while optimizing for the conversion performance. Our algorithm tries to select high quality impressions and adjust the bid price based on the prior performance distribution in an adaptive manner by distributing the budget optimally across time. Our experimental results from real advertising campaigns demonstrate the effectiveness of our proposed approach.

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"
    - "display advertising with real time bidding rtb and behavioural targeting"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "aiads automated and intelligent advertising system for sponsored search"):
  with members:
    - "deep reinforcement learning for sponsored search real time bidding"
    - "beyond keywords and relevance a personalized ad retrieval framework in e commerce sponsored search"
    - "roi constrained auctions"
    - "optimized cost per click in taobao display advertising"
    - "sponsored search auctions with rich ads"
    - "mechanism design for value maximizers"
    - "scalable semantic matching of queries to ads in sponsored search advertising"
    - "fast core pricing for rich advertising auctions"

Connected through hyperedge HE-3 (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 bidding for online advertising measurement and analysis"
    - "a dynamic near optimal algorithm for online linear programming"

Connected through hyperedge HE-4 (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 bidding for online advertising measurement and analysis"
    - "sequential selection of correlated ads by pomdps"

Connected through hyperedge HE-5 (induced by source paper "optimal delivery with budget constraint in e commerce advertising"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "smart pacing for effective online ad campaign optimization"
    - "ad serving using a compact allocation plan"

Connected through hyperedge HE-6 (induced by source paper "adaptive sequential experiments with unknown information flows"):
  with members:
    - "meta dynamic pricing learning across experiments"
    - "bandit learning with positive externalities"
    - "estimating the causal impact of recommendation systems from observational data"
    - "optimal exploration exploitation in a multi armed bandit problem with non stationary rewards"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "real time bidding benchmarking with ipinyou dataset"
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "further optimal regret bounds for thompson sampling"

Connected through hyperedge HE-7 (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"
    - "lift based bidding in ad selection"
    - "sequential click prediction for sponsored search with recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "deep reinforcement learning for sponsored search real time bidding"):
  with members:
    - "real time bidding by reinforcement learning in display advertising"
    - "ladder a human level bidding agent for large scale real time online auctions"
    - "continuous deep q learning with model based acceleration"
    - "multiagent cooperation and competition with deep reinforcement learning"
    - "real time bidding for online advertising measurement and analysis"
    - "playing atari with deep reinforcement learning"
    - "budget optimization for sponsored search censored learning in mdps"
    - "budget optimization in search based advertising auctions"

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)