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 "sequential predictions based on algorithmic 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 universal 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 convergence can be slow. In probabilistic environments, neither the posterior nor the losses converge, in general.

Includes members:
  Member "convergence of discrete mdl for sequential prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "open problems in universal induction intelligence")
  Member "on the convergence speed of mdl predictions for bernoulli sequences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "open problems in universal induction intelligence")
  Member "sequence prediction based on monotone complexity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "open problems in universal induction intelligence")
  Member "convergence and loss bounds for bayesian sequence prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ultimate intelligence part i physical completeness and objectivity of induction")
  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 "minimum description length induction bayesianism and kolmogorov complexity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "recursia rrt recursive translatable point set pattern discovery with removal of redundant translators")
  Member "new error bounds for solomonoff prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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.IT(Information Theory)