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: "effective complexity and its relation to logical depth"
  Abstract: Effective complexity measures the information content of the regularities of an object. It has been introduced by Gell-Mann and Lloyd to avoid some of the disadvantages of Kolmogorov complexity. In this paper, we derive a precise definition of effective complexity in terms of algorithmic information theory. We analyze rigorously its basic properties such as effective simplicity of incompressible binary strings and existence of strings that have effective complexity close to their lengths. Since some of the results have appeared independently in the context of algorithmic statistics by Gacs , we discuss the relation of effective complexity to the corresponding complexity measures, in particular to Kolmogorov minimal sufficient statistics. As our main new result we show a remarkable relation between effective complexity and Bennett's logical depth: If the effective complexity of a string x exceeds a certain explicit threshold then that string must have astronomically large depth; otherwise, the depth can be arbitrarily small.

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"
    - "sophistication vs 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 "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"
    - "a general notion of useful information"
    - "depth as randomness deficiency"

Connected through hyperedge HE-3 (induced by source paper "sophistication vs logical depth"):
  with members:
    - "kolmogorov s structure functions and model selection"

Connected through hyperedge HE-4 (induced by source paper "complexity distribution of agent policies"):
  with members:
    - "complexity and information measuring emergence self organization and homeostasis at multiple scales"

Connected through hyperedge HE-5 (induced by source paper "effective complexity of stationary process realizations"):
  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)