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: "pacing equilibrium in first price auction markets"
  Abstract: In ad auctions--the prevalent monetization mechanism of Internet companies--advertisers compete for online impressions in a sequential auction market. Since advertisers are typically budget-constrained, a common tool employed to improve their ROI is that of pacing, i.e., uniform scaling of their bids to preserve their budget for a longer duration. If the advertisers are excessively paced, they end up not spending their budget, while if they are not sufficiently paced, they use up their budget too soon. Therefore, it is important that they are paced at just the right amount, a solution concept that we call a pacing equilibrium. In this paper, we study pacing equilibria in the context of first-price auctions, which are popular in the theory of ad mechanisms. We show existence, uniqueness, and efficient computability of first-price pacing equilibria (FPPE), while also establishing several other salient features of this solution concept. In the process, we uncover a sharp contrast between these solutions and second price pacing equilibria (SPPE), the latter being known to produce non-unique, fragile solutions that are also computationally hard to obtain. Simulations show that FPPE have better revenue properties than SPPE, that bidders have lower ex-post regret, and that incentives to misreport budgets for thick markets are smaller.

Connected through hyperedge HE-1 (induced by source paper "stability and learning in strategic queuing systems"):
  with members:
    - "multiplicative pacing equilibria in 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 "computing large market equilibria using abstractions"):
  with members:
    - "markets for public decision making"
    - "deep counterfactual regret minimization"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "billion scale similarity search with gpus"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "convex program duality fisher markets and nash social welfare"
    - "imperfect recall abstractions with bounds in games"
    - "no regret learning in extensive form games with imperfect recall"

Connected through hyperedge HE-3 (induced by source paper "fair division without disparate impact"):
  with members:
    - "computing large market equilibria using abstractions"
    - "beyond parity fairness objectives for collaborative filtering"
    - "algorithmic decision making and the cost of fairness"
    - "convex program duality fisher markets and nash social welfare"
    - "on the im possibility of fairness"
    - "inherent trade offs in the fair determination of risk scores"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "fairness through awareness"

Connected through hyperedge HE-4 (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"
    - "multiplicative pacing equilibria in auction markets"
    - "convex program duality fisher markets and nash social welfare"
    - "market equilibrium with transaction costs"

Connected through hyperedge HE-5 (induced by source paper "scalable fair division for at most one preferences"):
  with members:
    - "computing large market equilibria using abstractions"
    - "fair division without disparate impact"
    - "convex program duality fisher markets and nash social welfare"
    - "the complexity of fairness through equilibrium"

Connected through hyperedge HE-6 (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"
    - "multiplicative pacing equilibria in auction markets"

Connected through hyperedge HE-7 (induced by source paper "cost per action constrained auctions"):
  with members:
    - "multiplicative pacing equilibria in auction markets"
    - "gsp the cinderella of mechanism design"
    - "real time bidding by reinforcement learning in display advertising"
    - "mechanism design for value maximizers"

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)