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: "algorithmic statistics revisited"
  Abstract: The mission of statistics is to provide adequate statistical hypotheses (models) for observed data. But what is an "adequate" model? To answer this question, one needs to use the notions of algorithmic information theory. It turns out that for every data string $x$ one can naturally define "stochasticity profile", a curve that represents a trade-off between complexity of a model and its adequacy. This curve has four different equivalent definitions in terms of (1)~randomness deficiency, (2)~minimal description length, (3)~position in the lists of simple strings and (4)~Kolmogorov complexity with decompression time bounded by busy beaver function. We present a survey of the corresponding definitions and results relating them to each other.

Connected through hyperedge HE-1 (induced by source paper "information kernels"):
  with members:
    - "algorithmic statistics forty years later"
    - "all sampling methods produce outliers"
    - "rate distortion and denoising of individual data using kolmogorov complexity"
    - "kolmogorov s structure functions and model selection"

Connected through hyperedge HE-2 (induced by source paper "on the algorithmic probability of sets"):
  with members:
    - "algorithmic statistics forty years later"
    - "all sampling methods produce outliers"
    - "kolmogorov s structure functions and model selection"

Connected through hyperedge HE-3 (induced by source paper "on the complexity of completing binary predicates"):
  with members:
    - "algorithmic statistics forty years later"
    - "all sampling methods produce outliers"
    - "kolmogorov s structure functions and model selection"

Connected through hyperedge HE-4 (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 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"

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)