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: "smart pacing for effective online ad campaign optimization"
  Abstract: In targeted online advertising, advertisers look for maximizing campaign performance under delivery constraint within budget schedule. Most of the advertisers typically prefer to impose the delivery constraint to spend budget smoothly over the time in order to reach a wider range of audiences and have a sustainable impact. Since lots of impressions are traded through public auctions for online advertising today, the liquidity makes price elasticity and bid landscape between demand and supply change quite dynamically. Therefore, it is challenging to perform smooth pacing control and maximize campaign performance simultaneously. In this paper, we propose a smart pacing approach in which the delivery pace of each campaign is learned from both offline and online data to achieve smooth delivery and optimal performance goals. The implementation of the proposed approach in a real DSP system is also presented. Experimental evaluations on both real online ad campaigns and offline simulations show that our approach can effectively improve campaign performance and achieve delivery goals.

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 "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-3 (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-4 (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"
    - "real time bidding for online advertising measurement and analysis"
    - "real time bid optimization with smooth budget delivery in online advertising"
    - "a dynamic near optimal algorithm for online linear programming"

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"
    - "display advertising with real time bidding rtb and behavioural targeting"
    - "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 "optimal delivery with budget constraint in e commerce advertising"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "real time bid optimization with smooth budget delivery in online advertising"
    - "ad serving using a compact allocation plan"

Connected through hyperedge HE-7 (induced by source paper "real time bidding by reinforcement learning in display advertising"):
  with members:
    - "display advertising with real time bidding rtb and behavioural targeting"
    - "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"
    - "sequential selection of correlated ads by pomdps"

Connected through hyperedge HE-8 (induced by source paper "finding needle in a million metrics anomaly detection in a large scale computational advertising platform"):
  with members:
    - "multi touch attribution based budget allocation in online advertising"
    - "online model evaluation in a large scale computational advertising platform"

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.AI(Artificial Intelligence)