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: "sophistication vs logical depth"
  Abstract: Sophistication and logical depth are two measures that express how complicated the structure in a string is. Sophistication is defined as the minimal complexity of a computable function that defines a two-part description for the string that is shortest within some precision; the second can be defined as the minimal computation time of a program that is shortest within some precision. We show that the Busy Beaver function of the sophistication of a string exceeds its logical depth with logarithmically bigger precision, and that logical depth exceeds the Busy Beaver function of sophistication with logarithmically bigger precision. We also show that this is not true if the precision is only increased by a constant (when the notions are defined with plain Kolmogorov complexity). Finally we show that sophistication is unstable in its precision: constant variations can change its value by a linear term in the length of the string.

Connected through hyperedge HE-1 (induced by source paper "relativity of depth and sophistication"):
  with members:
    - "algorithmic statistics forty years later"
    - "some properties of antistochastic strings"
    - "effective complexity and its relation to logical depth"
    - "on the equivalence between minimal sufficient statistics minimal typical models and initial segments of the halting sequence"

Connected through hyperedge HE-2 (induced by source paper "algorithmic statistics forty years later"):
  with members:
    - "algorithmic statistics normal objects and universal models"
    - "around kolmogorov complexity basic notions and results"
    - "algorithmic statistics revisited"
    - "algorithmic statistics prediction and machine learning"
    - "some properties of antistochastic strings"
    - "depth as randomness deficiency"
    - "rate distortion and denoising of individual data using kolmogorov complexity"
    - "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)