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 "the second price knapsack problem near optimal real time bidding in internet advertisement"):
  Abstract: In the past decade, Real Time Bidding (RTB) has become one of the most common purchase mechanisms of the online advertisement market. Under RTB a unique second-price auction between bidders is managed for every individual advertisement slot. We consider the bidder's problem of maximizing the value of the bundle of ads they purchase, subject to budget constraints, and assuming the value of each ad is known. We generalize the problem as a \textbf{second-price knapsack problem} with uncertain resource consumption: the bidder wins an auction when they bid the highest amount, but they pay an amount equal to the second-highest bid, unknown a priori. Surprisingly, because of the second-price mechanism, we prove that a linear form of bidding yields a near-optimal selection of ads. We extend to an implementable online one-shot learning algorithm. We prove that key points of the random permutation assumption hold in this setting, and that we can apply algorithms for the online-knapsack problem to this setting. Through this we recover a competitive ratio of $1-6\epsilon$, where $\epsilon$ is the training ratio and is small in general, yielding a strong theoretical result on an important variation of the standard online knapsack problem. Numerical results from the iPinYou dataset verify our results, recovering a bundle of ads with 99.5% - 100.0% of the optimal value.

Includes members:
  Member "bidding machine learning to bid for directly optimizing profits in display advertising":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sequential recommendation with dual side neighbor based collaborative relation modeling")
  Member "multiplicative pacing equilibria in auction markets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stability and learning in strategic queuing systems")
  Member "fast algorithms for online stochastic convex programming":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "simple and fast algorithm for binary integer and online linear programming")
  Member "real time bidding benchmarking with ipinyou dataset":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scalable bid landscape forecasting in real time bidding")
  Member "bandits with knapsacks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exploration exploitation in constrained mdps")
  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 "online stochastic packing applied to display ad allocation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "near optimal online algorithms and fast approximation algorithms for resource allocation problems")

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)