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 stationary prediction strategies"
  Abstract: In this paper we introduce the class of stationary prediction strategies and construct a prediction algorithm that asymptotically performs as well as the best continuous stationary strategy. We make mild compactness assumptions but no stochastic assumptions about the environment. In particular, no assumption of stationarity is made about the environment, and the stationarity of the considered strategies only means that they do not depend explicitly on time; we argue that it is natural to consider only stationary strategies even for highly non-stationary environments.

Connected through hyperedge HE-1 (induced by source paper "multi objective non parametric sequential prediction"):
  with members:
    - "nash equilibrium computation in subnetwork zero sum games with switching communications"

Connected through hyperedge HE-2 (induced by source paper "continuous and randomized defensive forecasting unified view"):
  with members:
    - "predictions as statements and decisions"
    - "uniform test of algorithmic randomness over a general space"

Connected through hyperedge HE-3 (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 markov 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-4 (induced by source paper "metric entropy in competitive on line prediction"):
  with members:
    - "on line prediction with kernels and the complexity approximation principle"
    - "leading strategies in competitive on line prediction"
    - "competing with markov prediction strategies"
    - "predictions as statements and decisions"
    - "non asymptotic calibration and resolution"
    - "competing with wild prediction rules"
    - "on line regression competitive with reproducing kernel hilbert spaces"

Connected through hyperedge HE-5 (induced by source paper "competing with markov prediction strategies"):
  with members:
    - "predictions as statements and decisions"

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)