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: "defensive forecasting for linear protocols"
  Abstract: We consider a general class of forecasting protocols, called "linear protocols", and discuss several important special cases, including multi-class forecasting. Forecasting is formalized as a game between three players: Reality, whose role is to generate observations; Forecaster, whose goal is to predict the observations; and Skeptic, who tries to make money on any lack of agreement between Forecaster's predictions and the actual observations. Our main mathematical result is that for any continuous strategy for Skeptic in a linear protocol there exists a strategy for Forecaster that does not allow Skeptic's capital to grow. This result is a meta-theorem that allows one to transform any continuous law of probability in a linear protocol into a forecasting strategy whose predictions are guaranteed to satisfy this law. We apply this meta-theorem to a weak law of large numbers in Hilbert spaces to obtain a version of the K29 prediction algorithm for linear protocols and show that this version also satisfies the attractive properties of proper calibration and resolution under a suitable choice of its kernel parameter, with no assumptions about the way the data is generated.

Connected through hyperedge HE-1 (induced by source paper "follow the leader if you can hedge if you must"):
  with members:
    - "prediction with advice of unknown number of experts"
    - "a parameter free hedging algorithm"
    - "adaptive online prediction by following the perturbed leader"

Connected through hyperedge HE-2 (induced by source paper "approachability regret and calibration implications and equivalences"):
  with members:
    - "response based approachability and its application to generalized no regret algorithms"
    - "internal regret with partial monitoring calibration based optimal algorithms"
    - "approachability of convex sets in games with partial monitoring"

Connected through hyperedge HE-3 (induced by source paper "defensive forecasting for optimal prediction with expert advice"):
  with members:
    - "predictions as statements and decisions"
    - "adaptive online prediction by following the perturbed leader"
    - "uniform test of algorithmic randomness over a general space"

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"
    - "competing with wild prediction rules"
    - "on line regression competitive with reproducing kernel hilbert spaces"

Connected through hyperedge HE-5 (induced by source paper "competitive on line learning with a convex loss function"):
  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)