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: "image characterization and classification by physical complexity"
  Abstract: We present a method for estimating the complexity of an image based on Bennett's concept of logical depth. Bennett identified logical depth as the appropriate measure of organized complexity, and hence as being better suited to the evaluation of the complexity of objects in the physical world. Its use results in a different, and in some sense a finer characterization than is obtained through the application of the concept of Kolmogorov complexity alone. We use this measure to classify images by their information content. The method provides a means for classifying and evaluating the complexity of objects by way of their visual representations. To the authors' knowledge, the method and application inspired by the concept of logical depth presented herein are being proposed and implemented for the first time.

Connected through hyperedge HE-1 (induced by source paper "natural scene statistics mediate the perception of image complexity"):
  with members:
    - "algorithmic complexity for short binary strings applied to psychology a primer"
    - "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-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"
    - "effective complexity and its relation to logical depth"
    - "a general notion of useful information"
    - "depth as randomness deficiency"

Connected through hyperedge HE-3 (induced by source paper "calculating kolmogorov complexity from the output frequency distributions of small turing machines"):
  with members:
    - "algorithmic complexity for short binary strings applied to psychology a primer"
    - "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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "complexity and information measuring emergence self organization and homeostasis at multiple scales"):
  with members:
    - "learning social intelligence and the turing test why an out of the box turing machine will not pass the turing test"
    - "the world as evolving information"
    - "numerical evaluation of algorithmic complexity for short strings a glance into the innermost structure of randomness"
    - "computing networks a general framework to contrast neural and swarm cognitions"
    - "compression based investigation of the dynamical properties of cellular automata and other systems"
    - "on the kolmogorov chaitin complexity for short sequences"
    - "classification of random boolean networks"

Connected through hyperedge HE-5 (induced by source paper "an algorithmic approach to information and meaning"):
  with members:
    - "numerical evaluation of algorithmic complexity for short strings a glance into the innermost structure of randomness"
    - "on the algorithmic nature of the world"
    - "towards a stable definition of kolmogorov chaitin complexity"
    - "on the kolmogorov chaitin complexity for short sequences"
    - "most programs stop quickly or never halt"
    - "shannon information and kolmogorov complexity"
    - "the similarity metric"

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)