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 "metric entropy in competitive on line prediction"):
  Abstract: Competitive on-line prediction (also known as universal prediction of individual sequences) is a strand of learning theory avoiding making any stochastic assumptions about the way the observations are generated. The predictor's goal is to compete with a benchmark class of prediction rules, which is often a proper Banach function space. Metric entropy provides a unifying framework for competitive on-line prediction: the numerous known upper bounds on the metric entropy of various compact sets in function spaces readily imply bounds on the performance of on-line prediction strategies. This paper discusses strengths and limitations of the direct approach to competitive on-line prediction via metric entropy, including comparisons to other approaches.

Includes members:
  Member "on line prediction with kernels and the complexity approximation principle":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an empirical comparison of algorithms for aggregating expert predictions")
  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 "leading strategies in competitive on line prediction":
    also connected through other hyperedges:
      (none)
  Member "competing with markov prediction strategies":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "leading strategies 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 "competing with wild prediction rules":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (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)