human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "aiads automated and intelligent advertising system for sponsored search"):
  Abstract: Sponsored search has more than 20 years of history, and it has been proven to be a successful business model for online advertising. Based on the pay-per-click pricing model and the keyword targeting technology, the sponsored system runs online auctions to determine the allocations and prices of search advertisements. In the traditional setting, advertisers should manually create lots of ad creatives and bid on some relevant keywords to target their audience. Due to the huge amount of search traffic and a wide variety of ad creations, the limits of manual optimizations from advertisers become the main bottleneck for improving the efficiency of this market. Moreover, as many emerging advertising forms and supplies are growing, it's crucial for sponsored search platform to pay more attention to the ROI metrics of ads for getting the marketing budgets of advertisers. In this paper, we present the AiAds system developed at Baidu, which use machine learning techniques to build an automated and intelligent advertising system. By designing and implementing the automated bidding strategy, the intelligent targeting and the intelligent creation models, the AiAds system can transform the manual optimizations into multiple automated tasks and optimize these tasks in advanced methods. AiAds is a brand-new architecture of sponsored search system which changes the bidding language and allocation mechanism, breaks the limit of keyword targeting with end-to-end ad retrieval framework and provides global optimization of ad creation. This system can increase the advertiser's campaign performance, the user experience and the revenue of the advertising platform simultaneously and significantly. We present the overall architecture and modeling techniques for each module of the system and share our lessons learned in solving several key challenges.

Includes members:
  Member "deep reinforcement learning for sponsored search real time bidding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "motiac multi objective actor critics for real time bidding")
  Member "beyond keywords and relevance a personalized ad retrieval framework in e commerce sponsored search":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "production ranking systems a review")
  Member "roi constrained auctions":
    also connected through other hyperedges:
      (none)
  Member "optimized cost per click in taobao display advertising":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "motiac multi objective actor critics for real time bidding")
  Member "sponsored search auctions with rich ads":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the ad types problem")
  Member "mechanism design for value maximizers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cost per action constrained auctions")
  Member "scalable semantic matching of queries to ads in sponsored search advertising":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an image is worth a thousand features scalable product representations for in session type ahead personalization")
  Member "fast core pricing for rich advertising auctions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the ad types problem")

Question: Please tell me which class the source paper 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.LG(Machine Learning)