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: "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.

Connected through hyperedge HE-1 (induced by source paper "open problems in universal induction intelligence"):
  with members:
    - "policy iteration for factored mdps"
    - "feature dynamic bayesian networks"
    - "online planning algorithms for pomdps"
    - "feature markov decision processes"
    - "on the possibility of learning in reactive environments with arbitrary dependence"
    - "a collection of definitions of intelligence"
    - "universal algorithmic intelligence a mathematical top down approach"
    - "tests of machine intelligence"

Connected through hyperedge HE-2 (induced by source paper "algorithmic complexity bounds on future prediction errors"):
  with members:
    - "monotone conditional complexity bounds on future prediction errors"
    - "sequential predictions based on algorithmic complexity"
    - "universal convergence of semimeasures on individual random sequences"
    - "sequence prediction based on monotone complexity"
    - "convergence and loss bounds for bayesian sequence prediction"
    - "optimality of universal bayesian sequence prediction for general loss and alphabet"
    - "general loss bounds for universal sequence prediction"
    - "convergence and error bounds for universal prediction of nonbinary sequences"

Connected through hyperedge HE-3 (induced by source paper "is there an elegant universal theory of prediction"):
  with members:
    - "on the foundations of universal sequence prediction"

Connected through hyperedge HE-4 (induced by source paper "asymptotics of discrete mdl for online prediction"):
  with members:
    - "adaptive online prediction by following the perturbed leader"
    - "on the convergence speed of mdl predictions for bernoulli sequences"
    - "sequence prediction based on monotone complexity"
    - "convergence and loss bounds for bayesian sequence prediction"
    - "the similarity metric"
    - "minimum description length induction bayesianism and kolmogorov complexity"
    - "new error bounds for solomonoff prediction"

Connected through hyperedge HE-5 (induced by source paper "sequential predictions based on algorithmic complexity"):
  with members:
    - "on the convergence speed of mdl predictions for bernoulli sequences"
    - "sequence prediction based on monotone complexity"
    - "convergence and loss bounds for bayesian sequence prediction"
    - "convergence and error bounds for universal prediction of nonbinary sequences"
    - "minimum description length induction bayesianism and kolmogorov complexity"
    - "new error bounds for solomonoff prediction"

Connected through hyperedge HE-6 (induced by source paper "monotone conditional complexity bounds on future prediction errors"):
  with members:
    - "universal convergence of semimeasures on individual random sequences"
    - "sequence prediction based on monotone complexity"
    - "convergence and loss bounds for bayesian sequence prediction"
    - "optimality of universal bayesian sequence prediction for general loss and alphabet"
    - "convergence and error bounds for universal prediction of nonbinary sequences"

Connected through hyperedge HE-7 (induced by source paper "on the convergence speed of mdl predictions for bernoulli sequences"):
  with members:
    - "sequential predictions based on algorithmic complexity"
    - "sequence prediction based on monotone complexity"
    - "convergence and loss bounds for bayesian sequence prediction"
    - "convergence and error bounds for universal prediction of nonbinary sequences"
    - "minimum description length induction bayesianism and kolmogorov complexity"

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)