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: "sequence prediction based on monotone complexity"
  Abstract: This paper studies sequence prediction based on the monotone Kolmogorov complexity Km=-log m, i.e. based on universal deterministic/one-part MDL. m is extremely close to Solomonoff's prior M, the latter being an excellent predictor in deterministic as well as probabilistic environments, where performance is measured in terms of convergence of posteriors or losses. Despite this closeness to M, it is difficult to assess the prediction quality of m, since little is known about the closeness of their posteriors, which are the important quantities for prediction. We show that for deterministic computable environments, the "posterior" and losses of m converge, but rapid convergence could only be shown on-sequence; the off-sequence behavior is unclear. In probabilistic environments, neither the posterior nor the losses converge, in general.

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"
    - "convergence of discrete mdl for sequential prediction"
    - "universal convergence of semimeasures on individual random sequences"
    - "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 "on semimeasures predicting martin loef random sequences"):
  with members:
    - "universal convergence of semimeasures on individual random sequences"
    - "minimum description length induction bayesianism and kolmogorov complexity"

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"
    - "convergence of discrete mdl for sequential prediction"
    - "on the convergence speed of mdl predictions for bernoulli sequences"
    - "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:
    - "convergence of discrete mdl for sequential prediction"
    - "on the convergence speed of mdl predictions for bernoulli sequences"
    - "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:
    - "convergence of discrete mdl for sequential prediction"
    - "universal convergence of semimeasures on individual random sequences"
    - "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 generalized computable universal priors and their convergence"):
  with members:
    - "universal convergence of semimeasures on individual random sequences"
    - "convergence and error bounds for universal prediction of nonbinary sequences"
    - "minimum description length induction bayesianism and kolmogorov complexity"

Connected through hyperedge HE-8 (induced by source paper "convergence of discrete mdl for sequential prediction"):
  with members:
    - "on the convergence speed of mdl predictions for bernoulli sequences"
    - "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.AI(Artificial Intelligence)