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 wild prediction rules"
  Abstract: We consider the problem of on-line prediction competitive with a benchmark class of continuous but highly irregular prediction rules. It is known that if the benchmark class is a reproducing kernel Hilbert space, there exists a prediction algorithm whose average loss over the first N examples does not exceed the average loss of any prediction rule in the class plus a "regret term" of O(N^(-1/2)). The elements of some natural benchmark classes, however, are so irregular that these classes are not Hilbert spaces. In this paper we develop Banach-space methods to construct a prediction algorithm with a regret term of O(N^(-1/p)), where p is in [2,infty) and p-2 reflects the degree to which the benchmark class fails to be a Hilbert space.

Connected through hyperedge HE-1 (induced by source paper "locally adaptive nonparametric online learning"):
  with members:
    - "introduction to online convex optimization"
    - "gated linear networks"
    - "lipschitz adaptivity with multiple learning rates in online learning"
    - "nonparametric online regression while learning the metric"
    - "metagrad multiple learning rates in online learning"
    - "coin betting and parameter free online learning"
    - "online learning via sequential complexities"
    - "metric entropy in competitive on line prediction"

Connected through hyperedge HE-2 (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"
    - "predictions as statements and decisions"

Connected through hyperedge HE-3 (induced by source paper "aggregating algorithm competing with banach lattices"):
  with members:
    - "on line prediction with kernels and the complexity approximation principle"
    - "metric entropy in competitive on line prediction"

Connected through hyperedge HE-4 (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"
    - "predictions as statements and decisions"
    - "non asymptotic calibration and resolution"
    - "competitive on line learning with a convex loss function"
    - "on line regression competitive with reproducing kernel hilbert spaces"
    - "defensive forecasting for linear protocols"

Connected through hyperedge HE-5 (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"
    - "predictions as statements and decisions"
    - "non asymptotic calibration and resolution"
    - "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)