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: "multiplicative pacing equilibria in auction markets"
  Abstract: Budgets play a significant role in real-world sequential auction markets such as those implemented by Internet companies. To maximize the value provided to auction participants, spending is smoothed across auctions so budgets are used for the best opportunities. This paper considers a smoothing procedure that relies on {\em pacing multipliers}: for each bidder, the platform applies a factor between 0 and 1 that uniformly scales the bids across all auctions. Looking at this process as a game between all bidders, we introduce the notion of {\em pacing equilibrium}, and prove that they are always guaranteed to exist. We demonstrate through examples that a market can have multiple pacing equilibria with large variations in several natural objectives. We go on to show that computing either a social-welfare-maximizing or a revenue-maximizing pacing equilibrium is NP-hard. Finally, we develop a mixed-integer program whose feasible solutions coincide with pacing equilibria, and show that it can be used to find equilibria that optimize several interesting objectives. Using our mixed-integer program, we perform numerical simulations on synthetic auction markets that provide evidence that, in spite of the possibility of equilibrium multiplicity, it occurs very rarely across several families of random instances. We also show that solutions from the mixed-integer program can be used to improve the outcomes achieved in a more realistic adaptive pacing setting.

Connected through hyperedge HE-1 (induced by source paper "stability and learning in strategic queuing systems"):
  with members:
    - "pacing equilibrium in first price auction markets"
    - "small loss bounds for online learning with partial information"
    - "regret of queueing bandits"
    - "the fitness level method with tail bounds"
    - "composable and efficient mechanisms"

Connected through hyperedge HE-2 (induced by source paper "online learning for measuring incentive compatibility in ad auctions"):
  with members:
    - "learning to bid without knowing your value"
    - "combinatorial multi armed bandit with general reward functions"
    - "online learning in repeated auctions"
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "online learning and profit maximization from revealed preferences"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "mechanism design for data science"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-3 (induced by source paper "robust market equilibria with uncertain preferences"):
  with members:
    - "computing large market equilibria using abstractions"
    - "fair division without disparate impact"
    - "robust multi agent counterfactual prediction"
    - "pacing equilibrium in first price auction markets"
    - "convex program duality fisher markets and nash social welfare"
    - "market equilibrium with transaction costs"

Connected through hyperedge HE-4 (induced by source paper "first order methods with increasing iterate averaging for solving saddle point problems"):
  with members:
    - "computing large market equilibria using abstractions"
    - "solving large sequential games with the excessive gap technique"
    - "online convex optimization for sequential decision processes and extensive form games"
    - "pacing equilibrium in first price auction markets"

Connected through hyperedge HE-5 (induced by source paper "cost per action constrained auctions"):
  with members:
    - "pacing equilibrium in first price auction markets"
    - "gsp the cinderella of mechanism design"
    - "real time bidding by reinforcement learning in display advertising"
    - "mechanism design for value maximizers"

Connected through hyperedge HE-6 (induced by source paper "the second price knapsack problem near optimal real time bidding in internet advertisement"):
  with members:
    - "bidding machine learning to bid for directly optimizing profits in display advertising"
    - "fast algorithms for online stochastic convex programming"
    - "real time bidding benchmarking with ipinyou dataset"
    - "bandits with knapsacks"
    - "real time bidding for online advertising measurement and analysis"
    - "real time bid optimization with smooth budget delivery in online advertising"
    - "online stochastic packing applied to display ad allocation"
    - "a dynamic near optimal algorithm for online linear programming"

Connected through hyperedge HE-7 (induced by source paper "budget constrained bidding by model free reinforcement learning in display advertising"):
  with members:
    - "real time bidding by reinforcement learning in display advertising"
    - "optimal real time bidding frameworks discussion"
    - "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 "pacing equilibrium in first price auction markets"):
  with members:
    - "computing large market equilibria using abstractions"
    - "fair division without disparate impact"
    - "convex program duality fisher markets and nash social welfare"
    - "on revenue maximization in second price ad auctions"
    - "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)