human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "convergence of discrete mdl for sequential prediction"):
  Abstract: We study the properties of the Minimum Description Length principle for sequence prediction, considering a two-part MDL estimator which is chosen from a countable class of models. This applies in particular to the important case of universal sequence prediction, where the model class corresponds to all algorithms for some fixed universal Turing machine (this correspondence is by enumerable semimeasures, hence the resulting models are stochastic). We prove convergence theorems similar to Solomonoff's theorem of universal induction, which also holds for general Bayes mixtures. The bound characterizing the convergence speed for MDL predictions is exponentially larger as compared to Bayes mixtures. We observe that there are at least three different ways of using MDL for prediction. One of these has worse prediction properties, for which predictions only converge if the MDL estimator stabilizes. We establish sufficient conditions for this to occur. Finally, some immediate consequences for complexity relations and randomness criteria are proven.

Includes members:
  Member "on the convergence speed of mdl predictions for bernoulli sequences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "open problems in universal induction intelligence")
  Member "sequence prediction based on monotone complexity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "open problems in universal induction intelligence")
  Member "optimality of universal bayesian sequence prediction for general loss and alphabet":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "thompson sampling for adversarial bit prediction")
  Member "the similarity metric":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "max margin class imbalanced learning with gaussian affinity")
  Member "convergence and error bounds for universal prediction of nonbinary sequences":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "concentration and confidence for discrete bayesian sequence predictors")
  Member "new error bounds for solomonoff prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bad universal priors and notions of optimality")

Question: Please tell me which class the source paper 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)