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 the convergence speed of mdl predictions for bernoulli sequences"
  Abstract: We consider the Minimum Description Length principle for online sequence prediction. If the underlying model class is discrete, then the total expected square loss is a particularly interesting performance measure: (a) this quantity is bounded, implying convergence with probability one, and (b) it additionally specifies a `rate of convergence'. Generally, for MDL only exponential loss bounds hold, as opposed to the linear bounds for a Bayes mixture. We show that this is even the case if the model class contains only Bernoulli distributions. We derive a new upper bound on the prediction error for countable Bernoulli classes. This implies a small bound (comparable to the one for Bayes mixtures) for certain important model classes. The results apply to many Machine Learning tasks including classification and hypothesis testing. We provide arguments that our theorems generalize to countable classes of i.i.d. models.

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 "on the foundations of universal sequence prediction"):
  with members:
    - "universal convergence of semimeasures on individual random sequences"
    - "optimal ordered problem solver"

Connected through hyperedge HE-3 (induced by source paper "asymptotics of discrete mdl for online prediction"):
  with members:
    - "adaptive online prediction by following the perturbed leader"
    - "convergence of discrete mdl for sequential prediction"
    - "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-4 (induced by source paper "sequential predictions based on algorithmic complexity"):
  with members:
    - "convergence of discrete mdl for sequential prediction"
    - "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-5 (induced by source paper "convergence of discrete mdl for sequential prediction"):
  with members:
    - "sequence prediction based on monotone complexity"
    - "optimality of universal bayesian sequence prediction for general loss and alphabet"
    - "the similarity metric"
    - "convergence and error bounds for universal prediction of nonbinary sequences"
    - "new error bounds for solomonoff prediction"

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)