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: "competitive on line learning with a convex loss function"
  Abstract: We consider the problem of sequential decision making under uncertainty in which the loss caused by a decision depends on the following binary observation. In competitive on-line learning, the goal is to design decision algorithms that are almost as good as the best decision rules in a wide benchmark class, without making any assumptions about the way the observations are generated. However, standard algorithms in this area can only deal with finite-dimensional (often countable) benchmark classes. In this paper we give similar results for decision rules ranging over an arbitrary reproducing kernel Hilbert space. For example, it is shown that for a wide class of loss functions (including the standard square, absolute, and log loss functions) the average loss of the master algorithm, over the first N observations, does not exceed the average loss of the best decision rule with a bounded norm plus O(N 1/2 ). Our proof technique is very different from the standard ones and is based on recent results about defensive forecasting. Given the probabilities produced by a defensive forecasting algorithm, which are known to be well calibrated and to have good resolution in the long run, we use the expected loss minimization principle to find a suitable decision.

Connected through hyperedge HE-1 (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"
    - "on line regression competitive with reproducing kernel hilbert spaces"
    - "uniform test of algorithmic randomness over a general space"

Connected through hyperedge HE-2 (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"
    - "competing with wild prediction rules"
    - "on line regression competitive with reproducing kernel hilbert spaces"
    - "defensive forecasting for linear protocols"

Connected through hyperedge HE-3 (induced by source paper "predictions as statements and decisions"):
  with members:
    - "non asymptotic calibration and resolution"
    - "uniform test of algorithmic randomness over a general space"

Connected through hyperedge HE-4 (induced by source paper "on line regression competitive with reproducing kernel hilbert spaces"):
  with members:
    - "relative loss bounds for on line density estimation with the exponential family of distributions"
    - "on line prediction with kernels and the complexity approximation principle"
    - "non asymptotic calibration and resolution"
    - "uniform test of algorithmic randomness over a general space"

Connected through hyperedge HE-5 (induced by source paper "competing with wild prediction rules"):
  with members:
    - "metric entropy in competitive on line prediction"
    - "on line regression competitive with reproducing kernel hilbert spaces"

Connected through hyperedge HE-6 (induced by source paper "defensive forecasting for linear protocols"):
  with members:
    - "non asymptotic calibration and resolution"

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)