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 short binary strings applied to psychology a primer"
  Abstract: As human randomness production has come to be more closely studied and used to assess executive functions (especially inhibition), many normative measures for assessing the degree to which a sequence is randomlike have been suggested. However, each of these measures focuses on one feature of randomness, leading researchers to have to use multiple measures. Although algorithmic complexity has been suggested as a means for overcoming this inconvenience, it has never been used, because standard Kolmogorov complexity is inapplicable to short strings (e.g., of length l ≤ 50), due to both computational and theoretical limitations. Here, we describe a novel technique (the coding theorem method) based on the calculation of a universal distribution, which yields an objective and universal measure of algorithmic complexity for short strings that approximates Kolmogorov–Chaitin complexity.

Connected through hyperedge HE-1 (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 psychology a user friendly implementation of the coding theorem method"
    - "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-2 (induced by source paper "the information theoretic and algorithmic approach to human animal and artificial cognition"):
  with members:
    - "algorithmic complexity for psychology a user friendly implementation of the coding theorem method"
    - "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-3 (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 psychology a user friendly implementation of the coding theorem method"
    - "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-4 (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 psychology a user friendly implementation of the coding theorem method"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "algorithmic complexity for psychology a user friendly implementation of the coding theorem method"):
  with members:
    - "natural scene statistics mediate the perception of image complexity"
    - "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"
    - "the google similarity distance"

Connected through hyperedge HE-6 (induced by source paper "natural scene statistics mediate the perception of image complexity"):
  with members:
    - "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"
    - "image characterization and classification by physical complexity"

Connected through hyperedge HE-7 (induced by source paper "calculating kolmogorov complexity from the output frequency distributions of small turing machines"):
  with members:
    - "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"
    - "two dimensional kolmogorov complexity and validation of the coding theorem method by compressibility"
    - "numerical evaluation of algorithmic complexity for short strings a glance into the innermost structure of randomness"
    - "image characterization and classification by physical complexity"
    - "compression based investigation of the dynamical properties of cellular automata and other systems"
    - "on the algorithmic nature of the world"
    - "towards a stable definition of kolmogorov chaitin complexity"

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)