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: "predictions as statements and decisions"
  Abstract: Prediction is a complex notion, and different predictors (such as people, computer programs, and probabilistic theories) can pursue very different goals. In this paper I will review some popular kinds of prediction and argue that the theory of competitive on-line learning can benefit from the kinds of prediction that are now foreign to it.

Connected through hyperedge HE-1 (induced by source paper "universal algorithm for online trading based on the method of calibration"):
  with members:
    - "supermartingales in prediction with expert advice"
    - "defensive forecasting for optimal prediction with expert advice"
    - "competing with wild prediction rules"

Connected through hyperedge HE-2 (induced by source paper "defensive forecasting for optimal prediction with expert advice"):
  with members:
    - "adaptive online prediction by following the perturbed leader"
    - "defensive forecasting for linear protocols"
    - "uniform test of algorithmic randomness over a general space"

Connected through hyperedge HE-3 (induced by source paper "continuous and randomized defensive forecasting unified view"):
  with members:
    - "competing with stationary prediction strategies"
    - "uniform test of algorithmic randomness over a general space"

Connected through hyperedge HE-4 (induced by source paper "hedging predictions in machine learning"):
  with members:
    - "uniform test of algorithmic randomness over a general space"

Connected through hyperedge HE-5 (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"
    - "competing with markov prediction strategies"
    - "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-6 (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"
    - "competing with markov prediction strategies"
    - "non asymptotic calibration and resolution"
    - "competing with wild prediction rules"
    - "on line regression competitive with reproducing kernel hilbert spaces"

Connected through hyperedge HE-7 (induced by source paper "competing with stationary prediction strategies"):
  with members:
    - "on line regression competitive with reproducing kernel hilbert spaces"

Connected through hyperedge HE-8 (induced by source paper "competing with markov prediction strategies"):
  with members:
    - "competing with stationary prediction strategies"

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)