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: "truthful learning mechanisms for multi slot sponsored search auctions with externalities"
  Abstract: Sponsored search auctions constitute one of the most successful applications of microeconomic mechanisms. In mechanism design, auctions are usually designed to incentivize advertisers to bid their truthful valuations and to assure both the advertisers and the auctioneer a non-negative utility. Nonetheless, in sponsored search auctions, the click-through-rates (CTRs) of the advertisers are often unknown to the auctioneer and thus standard truthful mechanisms cannot be directly applied and must be paired with an effective learning algorithm for the estimation of the CTRs. This introduces the critical problem of designing a learning mechanism able to estimate the CTRs at the same time as implementing a truthful mechanism with a revenue loss as small as possible compared to an optimal mechanism designed with the true CTRs. Previous work showed that, when dominant-strategy truthfulness is adopted, in single-slot auctions the problem can be solved using suitable exploration-exploitation mechanisms able to achieve a per-step regret (over the auctioneer's revenue) of order $O(T^{-1/3})$ (where T is the number of times the auction is repeated). It is also known that, when truthfulness in expectation is adopted, a per-step regret (over the social welfare) of order $O(T^{-1/2})$ can be obtained. In this paper we extend the results known in the literature to the case of multi-slot auctions. In this case, a model of the user is needed to characterize how the advertisers' valuations change over the slots. We adopt the cascade model that is the most famous model in the literature for sponsored search auctions. We prove a number of novel upper bounds and lower bounds both on the auctioneer's revenue loss and social welfare w.r.t. to the VCG auction and we report numerical simulations investigating the accuracy of the bounds in predicting the dependency of the regret on the auction parameters.

Connected through hyperedge HE-1 (induced by source paper "learning probably approximately correct maximin strategies in simulation based games with infinite strategy spaces"):
  with members:
    - "competitive bridge bidding with deep neural networks"
    - "multiagent evaluation under incomplete information"
    - "large scale learning of agent rationality in two player zero sum games"
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"
    - "a generalised method for empirical game theoretic analysis"
    - "bandit learning in concave n person games"
    - "what game are we playing end to end learning in normal and extensive form games"
    - "safe and nested subgame solving for imperfect information games"

Connected through hyperedge HE-2 (induced by source paper "personalized attraction enhanced sponsored search with multi task learning"):
  with members:
    - "deep reinforcement learning for sponsored search real time bidding"
    - "a survey on multi task learning"
    - "an overview of multi task learning in deep neural networks"
    - "learning multiple tasks with deep relationship networks"
    - "modeling attractiveness and multiple clicks in sponsored search results"
    - "distributed representations of words and phrases and their compositionality"
    - "budget optimization in search based advertising auctions"

Connected through hyperedge HE-3 (induced by source paper "a dominant strategy truthful deterministic multi armed bandit mechanism with logarithmic regret"):
  with members:
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "truthful mechanisms with implicit payment computation"
    - "characterizing truthful multi armed bandit mechanisms"

Connected through hyperedge HE-4 (induced by source paper "towards better models of externalities in sponsored search auctions"):
  with members:
    - "new inapproximability bounds for tsp"
    - "sponsored search auctions with markovian users"
    - "computationally feasible vcg mechanisms"

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)