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: "multi scale online learning and its applications to online auctions"
  Abstract: We consider revenue maximization in online auction/pricing problems. A seller sells an identical item in each period to a new buyer, or a new set of buyers. For the online posted pricing problem, we show regret bounds that scale with the best fixed price, rather than the range of the values. We also show regret bounds that are almost scale free, and match the offline sample complexity, when comparing to a benchmark that requires a lower bound on the market share. These results are obtained by generalizing the classical learning from experts and multi-armed bandit problems to their multi-scale versions. In this version, the reward of each action is in a different range, and the regret w.r.t. a given action scales with its own range, rather than the maximum range.

Connected through hyperedge HE-1 (induced by source paper "learning optimal reserve price against non myopic bidders"):
  with members:
    - "the price of differential privacy for online learning"
    - "learning simple auctions"
    - "jointly private convex programming"
    - "the sample complexity of revenue maximization"
    - "sampling and representation complexity of revenue maximization"
    - "making the most of your samples"
    - "perfect bayesian equilibria in repeated sales"
    - "learning prices for repeated auctions with strategic buyers"

Connected through hyperedge HE-2 (induced by source paper "black box reductions for parameter free online learning in banach spaces"):
  with members:
    - "online learning sufficient statistics and the burkholder method"
    - "metagrad multiple learning rates in online learning"
    - "simultaneous model selection and optimization through parameter free stochastic learning"
    - "unconstrained online linear learning in hilbert spaces minimax algorithms and normal approximations"
    - "a survey of algorithms and analysis for adaptive online learning"
    - "no regret algorithms for unconstrained online convex optimization"
    - "on the universality of online mirror descent"
    - "less regret via online conditioning"

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)