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 complexity for psychology a user friendly implementation of the coding theorem method"
  Abstract: Kolmogorov-Chaitin complexity has long been believed to be impossible to approximate when it comes to short sequences (e.g. of length 5-50). However, with the newly developed \emph{coding theorem method} the complexity of strings of length 2-11 can now be numerically estimated. We present the theoretical basis of algorithmic complexity for short strings (ACSS) and describe an R-package providing functions based on ACSS that will cover psychologists' needs and improve upon previous methods in three ways: (1) ACSS is now available not only for binary strings, but for strings based on up to 9 different symbols, (2) ACSS no longer requires time-consuming computing, and (3) a new approach based on ACSS gives access to an estimation of the complexity of strings of any length. Finally, three illustrative examples show how these tools can be applied to psychology.

Connected through hyperedge HE-1 (induced by source paper "algorithmic data analytics small data matters and correlation versus causation"):
  with members:
    - "low algorithmic complexity entropy deceiving graphs"
    - "numerical investigation of graph spectra and information interpretability of eigenvalues"
    - "natural scene statistics mediate the perception of image complexity"
    - "correlation of automorphism group size and topological properties with program size complexity evaluations of graphs and complex networks"
    - "correspondence and independence of numerical evaluations of algorithmic information measures"
    - "calculating kolmogorov complexity from the output frequency distributions of small turing machines"
    - "two dimensional kolmogorov complexity and validation of the coding theorem method by compressibility"
    - "on the algorithmic nature of the world"

Connected through hyperedge HE-2 (induced by source paper "slime mould the fundamental mechanisms of cognition"):
  with members:
    - "low algorithmic complexity entropy deceiving graphs"
    - "algorithmically probable mutations reproduce aspects of evolution such as convergence rate genetic memory and modularity"
    - "algorithmic complexity for short binary strings applied to psychology a primer"
    - "natural scene statistics mediate the perception of image complexity"
    - "on creativity of slime mould"
    - "physarum machine implementation of a kolmogorov uspensky machine on a biological substrate"

Connected through hyperedge HE-3 (induced by source paper "a decomposition method for global evaluation of shannon entropy and local estimations of algorithmic complexity"):
  with members:
    - "coding theorem like behaviour and emergence of the universal distribution from resource bounded algorithmic probability"
    - "low algorithmic complexity entropy deceiving graphs"
    - "natural scene statistics mediate the perception of image complexity"
    - "correlation of automorphism group size and topological properties with program size complexity evaluations of graphs and complex networks"
    - "calculating kolmogorov complexity from the output frequency distributions of small turing machines"
    - "on the kolmogorov chaitin complexity for short sequences"
    - "most programs stop quickly or never halt"

Connected through hyperedge HE-4 (induced by source paper "the information theoretic and algorithmic approach to human animal and artificial cognition"):
  with members:
    - "algorithmic complexity for short binary strings applied to psychology a primer"
    - "natural scene statistics mediate the perception of image complexity"
    - "correlation of automorphism group size and topological properties with program size complexity evaluations of graphs and complex networks"
    - "is consciousness computable quantifying integrated information using algorithmic information theory"
    - "what is nature like computation a behavioural approach and a notion of programmability"
    - "calculating kolmogorov complexity from the output frequency distributions of small turing machines"
    - "algorithmic simplicity and relevance"
    - "two dimensional kolmogorov complexity and validation of the coding theorem method by compressibility"

Connected through hyperedge HE-5 (induced by source paper "a computable measure of algorithmic probability by finite approximations"):
  with members:
    - "algorithmic data analytics small data matters and correlation versus causation"
    - "a decomposition method for global evaluation of shannon entropy and local estimations of algorithmic complexity"
    - "algorithmic complexity for short binary strings applied to psychology a primer"
    - "natural scene statistics mediate the perception of image complexity"
    - "correlation of automorphism group size and topological properties with program size complexity evaluations of graphs and complex networks"
    - "correspondence and independence of numerical evaluations of algorithmic information measures"
    - "calculating kolmogorov complexity from the output frequency distributions of small turing machines"
    - "two dimensional kolmogorov complexity and validation of the coding theorem method by compressibility"

Connected through hyperedge HE-6 (induced by source paper "a computable measure of algorithmic probability by finite approximations with an application to integer sequences"):
  with members:
    - "low algorithmic complexity entropy deceiving graphs"
    - "algorithmic complexity for short binary strings applied to psychology a primer"
    - "natural scene statistics mediate the perception of image complexity"
    - "correlation of automorphism group size and topological properties with program size complexity evaluations of graphs and complex networks"
    - "calculating kolmogorov complexity from the output frequency distributions of small turing machines"

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)