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: "online learning and profit maximization from revealed preferences"
  Abstract: We consider the problem of learning from revealed preferences in an online setting. In our framework, each period a consumer buys an optimal bundle of goods from a merchant according to her (linear) utility function and current prices, subject to a budget constraint. The merchant observes only the purchased goods, and seeks to adapt prices to optimize his profits. We give an efficient algorithm for the merchant's problem that consists of a learning phase in which the consumer's utility function is (perhaps partially) inferred, followed by a price optimization step. We also consider an alternative online learning algorithm for the setting where prices are set exogenously, but the merchant would still like to predict the bundle that will be bought by the consumer for purposes of inventory or supply chain management. In contrast with most prior work on the revealed preferences problem, we demonstrate that by making stronger assumptions on the form of utility functions, efficient algorithms for both learning and profit maximization are possible, even in adaptive, online settings.

Connected through hyperedge HE-1 (induced by source paper "grinding the space learning to classify against strategic agents"):
  with members:
    - "equal opportunity in online classification with partial feedback"
    - "distribution independent pac learning of halfspaces with massart noise"
    - "strategizing against no regret learners"
    - "introduction to multi armed bandits"
    - "nearly tight bounds for robust proper learning of halfspaces with a margin"
    - "competing prediction algorithms"
    - "strategyproof linear regression in high dimensions"
    - "strategic classification from revealed preferences"

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"
    - "multiplicative pacing equilibria in auction markets"
    - "combinatorial multi armed bandit with general reward functions"
    - "online learning in repeated auctions"
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "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 "learning to bid without knowing your value"):
  with members:
    - "bandits with movement costs and adaptive pricing"
    - "revenue optimization with approximate bid predictions"
    - "online learning with feedback graphs without the graphs"
    - "the sample complexity of revenue maximization"
    - "learning and efficiency in games with dynamic population"
    - "online learning in repeated auctions"
    - "online learning with feedback graphs beyond bandits"
    - "taming the monster a fast and simple algorithm for contextual bandits"

Connected through hyperedge HE-4 (induced by source paper "social welfare and profit maximization from revealed preferences"):
  with members:
    - "introduction to online convex optimization"
    - "strategic classification from revealed preferences"
    - "multidimensional dynamic pricing for welfare maximization"
    - "watch and learn optimizing from revealed preferences feedback"
    - "learning economic parameters from revealed preferences"
    - "linear coupling an ultimate unification of gradient and mirror descent"
    - "fast algorithms for online stochastic convex programming"
    - "learning what s going on reconstructing preferences and priorities from opaque transactions"

Connected through hyperedge HE-5 (induced by source paper "strategic classification from revealed preferences"):
  with members:
    - "kernel based methods for bandit convex optimization"
    - "multidimensional dynamic pricing for welfare maximization"
    - "watch and learn optimizing from revealed preferences feedback"
    - "learning from rational behavior predicting solutions to unknown linear programs"
    - "learning economic parameters from revealed preferences"
    - "efficiently learning from revealed preference"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-6 (induced by source paper "multidimensional dynamic pricing for welfare maximization"):
  with members:
    - "do prices coordinate markets"
    - "bandit convex optimization sqrt t regret in one dimension"
    - "watch and learn optimizing from revealed preferences feedback"
    - "learning economic parameters from revealed preferences"
    - "combinatorial auctions via posted prices"
    - "bandits with knapsacks"
    - "efficiently learning from revealed preference"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-7 (induced by source paper "online learning in repeated auctions"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "the sample complexity of revenue maximization"
    - "adaptive online learning"
    - "optimal auctions for correlated buyers with sampling"
    - "mechanism design for data science"
    - "learning valuation distributions from partial observation"
    - "bandits with knapsacks"
    - "learning theory and algorithms for revenue optimization in second price auctions with reserve"

Connected through hyperedge HE-8 (induced by source paper "watch and learn optimizing from revealed preferences feedback"):
  with members:
    - "the sample complexity of revenue maximization"
    - "escaping the local minima via simulated annealing optimization of approximately convex functions"
    - "the pseudo dimension of near optimal auctions"
    - "learning economic parameters from revealed preferences"
    - "sampling and representation complexity of revenue maximization"
    - "achieving target equilibria in network routing games without knowing the latency functions"
    - "making the most of your samples"
    - "mechanism design for data science"

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.DS(Data Structures and Algorithms)