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: "learning prices for repeated auctions with strategic buyers"
  Abstract: Inspired by real-time ad exchanges for online display advertising, we consider the problem of inferring a buyer's value distribution for a good when the buyer is repeatedly interacting with a seller through a posted-price mechanism. We model the buyer as a strategic agent, whose goal is to maximize her long-term surplus, and we are interested in mechanisms that maximize the seller's long-term revenue. We define the natural notion of strategic regret --- the lost revenue as measured against a truthful (non-strategic) buyer. We present seller algorithms that are no-(strategic)-regret when the buyer discounts her future surplus --- i.e. the buyer prefers showing advertisements to users sooner rather than later. We also give a lower bound on strategic regret that increases as the buyer's discounting weakens and shows, in particular, that any seller algorithm will suffer linear strategic regret if there is no discounting.

Connected through hyperedge HE-1 (induced by source paper "dynamic reserve prices for repeated auctions learning from bids"):
  with members:
    - "the sample complexity of revenue maximization"
    - "the sample complexity of auctions with side information"
    - "optimal auctions for correlated buyers with sampling"
    - "making the most of your samples"
    - "perfect bayesian equilibria in repeated sales"
    - "revenue optimization in posted price auctions with strategic buyers"
    - "learning theory and algorithms for revenue optimization in second price auctions with reserve"
    - "efficient nash computation in large population games with bounded influence"

Connected through hyperedge HE-2 (induced by source paper "dynamic incentive aware learning robust pricing in contextual auctions"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "contextual search via intrinsic volumes"
    - "multi armed bandit problems with strategic arms"
    - "perishability of data dynamic pricing under varying coefficient models"
    - "a field guide to personalized reserve prices"
    - "multidimensional binary search for contextual decision making"
    - "a nearly linear time ptas for explicit fractional packing and covering linear programs"
    - "learning theory and algorithms for revenue optimization in second price auctions with reserve"

Connected through hyperedge HE-3 (induced by source paper "protecting consumers against personalized pricing a stopping time approach"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "dynamic incentive aware learning robust pricing in contextual auctions"
    - "semi parametric dynamic contextual pricing"

Connected through hyperedge HE-4 (induced by source paper "reserve pricing in repeated second price auctions with strategic bidders"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "optimal pricing in repeated posted price auctions"
    - "learning optimal reserve price against non myopic bidders"
    - "contextual search via intrinsic volumes"
    - "robust repeated auctions under heterogeneous buyer behavior"
    - "optimal dynamic auctions are virtual welfare maximizers"
    - "dynamic pricing with finitely many unknown valuations"
    - "on consistency of optimal pricing algorithms in repeated posted price auctions with strategic buyer"

Connected through hyperedge HE-5 (induced by source paper "bayesian exploration with heterogeneous agents"):
  with members:
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "the externalities of exploration and how data diversity helps exploitation"
    - "overabundant information and learning traps"
    - "incentivizing exploration with unbiased histories"
    - "competing bandits learning under competition"
    - "fairness incentives for myopic agents"
    - "optimal and myopic information acquisition"
    - "selling to a no regret buyer"

Connected through hyperedge HE-6 (induced by source paper "robust stackelberg buyers in repeated auctions"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "dynamic incentive aware learning robust pricing in contextual auctions"
    - "thresholding the virtual value a simple method to increase welfare and lower reserve prices in online auction systems"
    - "thresholding at the monopoly price an agnostic way to improve bidding strategies in revenue maximizing auctions"
    - "optimal auctions through deep learning"
    - "a field guide to personalized reserve prices"
    - "sequential mechanisms with ex post participation guarantees"
    - "the sample complexity of revenue maximization"

Connected through hyperedge HE-7 (induced by source paper "incentive aware contextual pricing with non parametric market noise"):
  with members:
    - "dynamic incentive aware learning robust pricing in contextual auctions"
    - "the intrinsic robustness of stochastic bandits to strategic manipulation"
    - "lp based approximation for personalized reserve prices"
    - "semi parametric dynamic contextual pricing"
    - "nonparametric learning and optimization with covariates"
    - "learning optimal reserve price against non myopic bidders"
    - "contextual search via intrinsic volumes"
    - "ironing in the dark"

Connected through hyperedge HE-8 (induced by source paper "the perils of exploration under competition a computational modeling approach"):
  with members:
    - "competing bandits learning under competition"
    - "a tutorial on thompson sampling"
    - "selling to a no regret buyer"
    - "making contextual decisions with low technical debt"
    - "bayesian incentive compatible bandit exploration"
    - "adaptive contract design for crowdsourcing markets bandit algorithms for repeated principal agent problems"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "truthful mechanisms with implicit payment computation"

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.LG(Machine Learning)