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: "around kolmogorov complexity basic notions and results"
  Abstract: Algorithmic information theory studies description complexity and randomness and is now a well-known field of theoretical computer science and mathematical logic. There are several textbooks and monographs devoted to this theory (Calude, Information and Randomness. An Algorithmic Perspective, 2002, Downey and Hirschfeldt, Algorithmic Randomness and Complexity, 2010, Li and Vitanyi, An Introduction to Kolmogorov Complexity and Its Applications, 2008, Nies, Computability and Randomness, 2009, Vereshchagin et al., Kolmogorov Complexity and Algorithmic Randomness, in Russian, 2013) where one can find a detailed exposition of many difficult results as well as historical references. However, it seems that a short survey of its basic notions and main results relating these notions to each other is missing. This chapter attempts to fill this gap and covers the basic notions of algorithmic information theory : Kolmogorov complexity (plain, conditional, prefix), Solomonoff universal a priori probability, notions of randomness (Martin-Lof randomness, Mises–Church randomness), and effective Hausdorff dimension . We prove their basic properties (symmetry of information, connection between a priori probability and prefix complexity, criterion of randomness in terms of complexity, complexity characterization for effective dimension) and show some applications ( incompressibility method in computational complexity theory, incompleteness theorems). The chapter is based on the lecture notes of a course at Uppsala University given by the author (Shen, Algorithmic information theory and Kolmogorov complexity. Technical Report, 2000).

Connected through hyperedge HE-1 (induced by source paper "automatic kolmogorov complexity normality and finite state dimension revisited"):
  with members:
    - "automatic kolmogorov complexity and normality revisited"
    - "finite state independence"
    - "nondeterministic automatic complexity of overlap free and almost square free words"
    - "finite state dimension and real arithmetic"
    - "dimension in complexity classes"

Connected through hyperedge HE-2 (induced by source paper "on algorithmic statistics for space bounded algorithms"):
  with members:
    - "algorithmic statistics forty years later"
    - "improving the space bounded version of muchnik s conditional complexity theorem via naive derandomization"
    - "rate distortion and denoising of individual data using kolmogorov complexity"
    - "kolmogorov s structure functions and model selection"

Connected through hyperedge HE-3 (induced by source paper "automatic kolmogorov complexity and normality revisited"):
  with members:
    - "nondeterministic automatic complexity of overlap free and almost square free words"

Connected through hyperedge HE-4 (induced by source paper "algorithmic statistics forty years later"):
  with members:
    - "algorithmic statistics normal objects and universal models"
    - "algorithmic statistics revisited"
    - "algorithmic statistics prediction and machine learning"
    - "some properties of antistochastic strings"
    - "sophistication vs logical depth"
    - "depth as randomness deficiency"
    - "rate distortion and denoising of individual data using kolmogorov complexity"
    - "kolmogorov s structure functions and model selection"

Connected through hyperedge HE-5 (induced by source paper "universal probability free prediction"):
  with members:
    - "universal probability free conformal prediction"

Connected through hyperedge HE-6 (induced by source paper "algorithmic statistics normal objects and universal models"):
  with members:
    - "kolmogorov s structure functions and model selection"

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.IT(Information Theory)