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 "bid optimization by multivariable control in display advertising"):
  Abstract: Real-Time Bidding (RTB) is an important paradigm in display advertising, where advertisers utilize extended information and algorithms served by Demand Side Platforms (DSPs) to improve advertising performance. A common problem for DSPs is to help advertisers gain as much value as possible with budget constraints. However, advertisers would routinely add certain key performance indicator (KPI) constraints that the advertising campaign must meet due to practical reasons. In this paper, we study the common case where advertisers aim to maximize the quantity of conversions, and set cost-per-click (CPC) as a KPI constraint. We convert such a problem into a linear programming problem and leverage the primal-dual method to derive the optimal bidding strategy. To address the applicability issue, we propose a feedback control-based solution and devise the multivariable control system. The empirical study based on real-word data from Taobao.com verifies the effectiveness and superiority of our approach compared with the state of the art in the industry practices.

Includes members:
  Member "budget constrained bidding by model free reinforcement learning in display advertising":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "jointly learning to recommend and advertise")
  Member "bidding machine learning to bid for directly optimizing profits in display advertising":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sequential recommendation with dual side neighbor based collaborative relation modeling")
  Member "real time bidding by reinforcement learning in display advertising":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "jointly learning to recommend and advertise")
  Member "feedback control of real time display advertising":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online joint bid daily budget optimization of internet advertising campaigns")
  Member "smart pacing for effective online ad campaign optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "motiac multi objective actor critics for real time bidding")
  Member "real time bidding for online advertising measurement and analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "motiac multi objective actor critics for real time bidding")
  Member "real time bid optimization with smooth budget delivery in online advertising":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "online joint bid daily budget optimization of internet advertising campaigns")
  Member "a dynamic near optimal algorithm for online linear programming":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "random order models")

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.GT(Computer Science and Game Theory)