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: "on line regression competitive with reproducing kernel hilbert spaces"
  Abstract: We consider the problem of on-line prediction of real-valued labels, assumed bounded in absolute value by a known constant, of new objects from known labeled objects. The prediction algorithm's performance is measured by the squared deviation of the predictions from the actual labels. No stochastic assumptions are made about the way the labels and objects are generated. Instead, we are given a benchmark class of prediction rules some of which are hoped to produce good predictions. We show that for a wide range of infinite-dimensional benchmark classes one can construct a prediction algorithm whose cumulative loss over the first N examples does not exceed the cumulative loss of any prediction rule in the class plus O(sqrt(N)); the main differences from the known results are that we do not impose any upper bound on the norm of the considered prediction rules and that we achieve an optimal leading term in the excess loss of our algorithm. If the benchmark class is "universal" (dense in the class of continuous functions on each compact set), this provides an on-line non-stochastic analogue of universally consistent prediction in non-parametric statistics. We use two proof techniques: one is based on the Aggregating Algorithm and the other on the recently developed method of defensive forecasting.

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 "nonparametric online regression while learning the metric"):
  with members:
    - "a survey on metric learning for feature vectors and structured data"

Connected through hyperedge HE-3 (induced by source paper "online learning with multiple operator valued kernels"):
  with members:
    - "online learning of noisy data with kernels"

Connected through hyperedge HE-4 (induced by source paper "supermartingales in prediction with expert advice"):
  with members:
    - "prediction with expert evaluators advice"
    - "prediction with expert advice for the brier game"
    - "continuous and randomized defensive forecasting unified view"
    - "defensive forecasting for optimal prediction with expert advice"
    - "metric entropy in competitive on line prediction"
    - "competitive on line learning with a convex loss function"
    - "uniform test of algorithmic randomness over a general space"

Connected through hyperedge HE-5 (induced by source paper "prediction with expert advice under discounted loss"):
  with members:
    - "prediction with advice of unknown number of experts"
    - "on line prediction with kernels and the complexity approximation principle"
    - "supermartingales in prediction with expert advice"
    - "a parameter free hedging algorithm"
    - "a new hedging algorithm and its application to inferring latent random variables"

Connected through hyperedge HE-6 (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"
    - "competing with wild prediction rules"
    - "defensive forecasting for linear protocols"

Connected through hyperedge HE-7 (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"
    - "competing with wild prediction rules"

Connected through hyperedge HE-8 (induced by source paper "competing with stationary 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)