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 in repeated auctions"
  Abstract: Motivated by online advertising auctions, we consider repeated Vickrey auctions where goods of unknown value are sold sequentially and bidders only learn (potentially noisy) information about a good's value once it is purchased. We adopt an online learning approach with bandit feedback to model this problem and derive bidding strategies for two models: stochastic and adversarial. In the stochastic model, the observed values of the goods are random variables centered around the true value of the good. In this case, logarithmic regret is achievable when competing against well behaved adversaries. In the adversarial model, the goods need not be identical and we simply compare our performance against that of the best fixed bid in hindsight. We show that sublinear regret is also achievable in this case and prove matching minimax lower bounds. To our knowledge, this is the first complete set of strategies for bidders participating in auctions of this type.

Connected through hyperedge HE-1 (induced by source paper "corrupted multidimensional binary search learning in the presence of irrational agents"):
  with members:
    - "stochastic linear optimization with adversarial corruption"
    - "corruption robust exploration in episodic reinforcement learning"
    - "better algorithms for stochastic bandits with adversarial corruptions"
    - "the intrinsic robustness of stochastic bandits to strategic manipulation"
    - "semi parametric dynamic contextual pricing"
    - "manipulating a learning defender and ways to counteract"
    - "contextual search via intrinsic volumes"
    - "stochastic bandits robust to adversarial corruptions"

Connected through hyperedge HE-2 (induced by source paper "a farewell to arms sequential reward maximization on a budget with a giving up option"):
  with members:
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "logarithmic regret bounds for bandits with knapsacks"
    - "bandits with knapsacks"
    - "online learning under delayed feedback"
    - "knapsack based optimal policies for budget limited multi armed bandits"

Connected through hyperedge HE-3 (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-4 (induced by source paper "learning to bid in revenue maximizing auctions"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "explicit shading strategies for repeated truthful 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"
    - "learning to bid without knowing your value"
    - "optimal auctions through deep learning"
    - "a field guide to personalized reserve prices"
    - "sequential mechanisms with ex post participation guarantees"

Connected through hyperedge HE-5 (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"
    - "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-6 (induced by source paper "contextual bandits with cross learning"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "incentive aware contextual pricing with non parametric market noise"
    - "learning to bid without knowing your value"
    - "learning multi item auctions with or without samples"
    - "selling to a no regret buyer"
    - "learning simple auctions"
    - "no regret learning in bayesian games"
    - "online learning with feedback graphs beyond bandits"

Connected through hyperedge HE-7 (induced by source paper "explicit shading strategies for repeated truthful auctions"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "a field guide to personalized reserve prices"
    - "learning simple auctions"
    - "how to manipulate truthful prior dependent mechanisms"
    - "the sample complexity of revenue maximization"
    - "learning in auctions regret is hard envy is easy"
    - "learning valuation distributions from partial observation"
    - "learning theory and algorithms for revenue optimization in second price auctions with reserve"

Connected through hyperedge HE-8 (induced by source paper "dynamic pricing with finitely many unknown valuations"):
  with members:
    - "fast rates for bandit optimization with upper confidence frank wolfe"
    - "bandits with concave rewards and convex knapsacks"
    - "perfect bayesian equilibria in repeated sales"
    - "bandits with knapsacks"
    - "learning prices for repeated auctions with strategic buyers"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "dynamic pricing with limited supply"

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)