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 forty years later"
  Abstract: Algorithmic statistics has two different (and almost orthogonal) motivations. From the philosophical point of view, it tries to formalize how the statistics works and why some statistical models are better than others. After this notion of a "good model" is introduced, a natural question arises: it is possible that for some piece of data there is no good model? If yes, how often these bad ("non-stochastic") data appear "in real life"? #R##N#Another, more technical motivation comes from algorithmic information theory. In this theory a notion of complexity of a finite object (=amount of information in this object) is introduced; it assigns to every object some number, called its algorithmic complexity (or Kolmogorov complexity). Algorithmic statistic provides a more fine-grained classification: for each finite object some curve is defined that characterizes its behavior. It turns out that several different definitions give (approximately) the same curve. #R##N#In this survey we try to provide an exposition of the main results in the field (including full proofs for the most important ones), as well as some historical comments. We assume that the reader is familiar with the main notions of algorithmic information (Kolmogorov complexity) theory.

Connected through hyperedge HE-1 (induced by source paper "approximations of kolmogorov complexity"):
  with members:
    - "on the complexity of completing binary predicates"
    - "list approximation for increasing kolmogorov complexity"
    - "short lists with short programs in short time"
    - "all sampling methods produce outliers"

Connected through hyperedge HE-2 (induced by source paper "information kernels"):
  with members:
    - "algorithmic statistics revisited"
    - "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-3 (induced by source paper "relativity of depth and sophistication"):
  with members:
    - "some properties of antistochastic strings"
    - "sophistication vs logical depth"
    - "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-4 (induced by source paper "on the algorithmic probability of sets"):
  with members:
    - "algorithmic statistics revisited"
    - "all sampling methods produce outliers"
    - "kolmogorov s structure functions and model selection"

Connected through hyperedge HE-5 (induced by source paper "on the complexity of completing binary predicates"):
  with members:
    - "algorithmic statistics revisited"
    - "all sampling methods produce outliers"
    - "kolmogorov s structure functions and model selection"

Connected through hyperedge HE-6 (induced by source paper "random noise increases kolmogorov complexity and hausdorff dimension"):
  with members:
    - "analysis of boolean functions"
    - "rate distortion and denoising of individual data using kolmogorov complexity"

Connected through hyperedge HE-7 (induced by source paper "on algorithmic statistics for space bounded algorithms"):
  with members:
    - "around kolmogorov complexity basic notions and results"
    - "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"

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.CC(Computational Complexity)