human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "leading strategies in competitive on line prediction"):
  Abstract: We start from a simple asymptotic result for the problem of on-line regression with the quadratic loss function: the class of continuous limited-memory prediction strategies admits a "leading prediction strategy", which not only asymptotically performs at least as well as any continuous limited-memory strategy but also satisfies the property that the excess loss of any continuous limited-memory strategy is determined by how closely it imitates the leading strategy. More specifically, for any class of prediction strategies constituting a reproducing kernel Hilbert space we construct a leading strategy, in the sense that the loss of any prediction strategy whose norm is not too large is determined by how closely it imitates the leading strategy. This result is extended to the loss functions given by Bregman divergences and by strictly proper scoring rules.

Includes members:
  Member "relative loss bounds for on line density estimation with the exponential family of distributions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "competing with stationary prediction strategies":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi objective non parametric sequential prediction")
  Member "competing with markov prediction strategies":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "metric entropy in competitive on line prediction")
  Member "predictions as statements and decisions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "universal algorithm for online trading based on the method of calibration")
  Member "non asymptotic calibration and resolution":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "calibration and internal no regret with partial monitoring")
  Member "competitive on line learning with a convex loss function":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "supermartingales in prediction with expert advice")
  Member "competing with wild prediction rules":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "locally adaptive nonparametric online learning")
  Member "on line regression competitive with reproducing kernel hilbert spaces":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "locally adaptive nonparametric online learning")

Question: Please tell me which class the source paper 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)