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: "competing with markov prediction strategies"
  Abstract: Assuming that the loss function is convex in the prediction, we construct a prediction strategy universal for the class of Markov prediction strategies, not necessarily continuous. Allowing randomization, we remove the requirement of convexity.

Connected through hyperedge HE-1 (induced by source paper "leading strategies in competitive on line prediction"):
  with members:
    - "relative loss bounds for on line density estimation with the exponential family of distributions"
    - "competing with stationary prediction strategies"
    - "predictions as statements and decisions"
    - "non asymptotic calibration and resolution"
    - "competitive on line learning with a convex loss function"
    - "competing with wild prediction rules"
    - "on line regression competitive with reproducing kernel hilbert spaces"
    - "defensive forecasting for linear protocols"

Connected through hyperedge HE-2 (induced by source paper "metric entropy in competitive on line prediction"):
  with members:
    - "on line prediction with kernels and the complexity approximation principle"
    - "competing with stationary prediction strategies"
    - "leading strategies in competitive on line prediction"
    - "predictions as statements and decisions"
    - "non asymptotic calibration and resolution"
    - "competing with wild prediction rules"
    - "on line regression competitive with reproducing kernel hilbert spaces"

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.LG(Machine Learning)