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: "depth as randomness deficiency"
  Abstract: Depth of an object concerns a tradeoff between computation time and excess of program length over the shortest program length required to obtain the object. It gives an unconditional lower bound on the computation time from a given program in absence of auxiliary information. Variants known as logical depth and computational depth are expressed in Kolmogorov complexity theory. #R##N#We derive quantitative relation between logical depth and computational depth and unify the different depth notions by relating them to A. Kolmogorov and L. Levin's fruitful notion of randomness deficiency. Subsequently, we revisit the computational depth of infinite strings, introducing the notion of super deep sequences and relate it with other approaches.

Connected through hyperedge HE-1 (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"
    - "sophistication vs logical depth"
    - "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 "depth highness and dnr degrees"):
  with members:
    - "on the polynomial depth of various sets of random strings"

Connected through hyperedge HE-3 (induced by source paper "unpredictability and computational irreducibility"):
  with members:
    - "numerical evaluation of algorithmic complexity for short strings a glance into the innermost structure of randomness"
    - "empirical encounters with computational irreducibility and unpredictability"
    - "image characterization and classification by physical complexity"
    - "effective complexity and its relation to logical depth"
    - "a general notion of useful information"

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)