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: "on the kolmogorov chaitin complexity for short sequences"
  Abstract: This is a presentation about joint work between Hector Zenil and Jean-Paul Delahaye. Zenil presents Experimental Algorithmic Theory as Algorithmic Information Theory and NKS, put together in a mixer. Algorithmic Complexity Theory defines the algorithmic complexity k(s) as the length of the shortest program that produces s. But since finding this short program is in general an undecidable question, the only way to approach k(s) is to use compression algorithms. He shows how to use the Compress function in Mathematica to give an idea about the compressibility of various sequences. However, the idea of applying a compression algorithm breaks down for very short sequences. This is true not only for the Compress function, but also for any other compression algorithm. Zenil's approach is to construct a metric of algorithmic complexity for short sequences from scratch. He defines the algorithmic probability as the probability that an arbitrary program produces a sequence. The basic idea is to run a whole class of computational devices such as Turing Machines or Cellular Automata, and compute the distributions of the sequences they generate. Zenil presents a comparison of frequency distributions of sequences generated by 2-state 3-color Turing Machines and 2-color radius 1 Cellular Automata. He also compared these distributions to distributions found in data from the real world, and found that not only there is correlation across different systems, but also that the distributions are rather stable, and the difference between the distributions in abstract systems and real-world data can be attributed to noise. In his paper Zenil elaborates on the nature of the noise he has encountered. Zenil conjectures that the correlation distances between different systems decreases with a larger number of steps, and converge in the infinite limit case.

Connected through hyperedge HE-1 (induced by source paper "compression is comprehension and the unreasonable effectiveness of digital computation in the natural world"):
  with members:
    - "coding theorem like behaviour and emergence of the universal distribution from resource bounded algorithmic probability"
    - "low algorithmic complexity entropy deceiving graphs"
    - "a decomposition method for global evaluation of shannon entropy and local estimations of algorithmic complexity"
    - "turing patterns with turing machines emergence and low level structure formation"

Connected through hyperedge HE-2 (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"
    - "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"
    - "most programs stop quickly or never halt"

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 short binary strings applied to psychology a primer"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "correlation of automorphism group size and topological properties with program size complexity evaluations of graphs and complex networks"):
  with members:
    - "correspondence and independence of numerical evaluations of algorithmic information measures"
    - "conflict anticipation in the search for graph automorphisms"
    - "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"
    - "numerical evaluation of algorithmic complexity for short strings a glance into the innermost structure of randomness"
    - "on the algorithmic nature of the world"
    - "complexity of networks"

Connected through hyperedge HE-5 (induced by source paper "correspondence and independence of numerical evaluations of algorithmic information measures"):
  with members:
    - "two dimensional kolmogorov complexity and validation of the coding theorem method by compressibility"
    - "towards a stable definition of kolmogorov chaitin complexity"
    - "most programs stop quickly or never halt"

Connected through hyperedge HE-6 (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"
    - "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"

Connected through hyperedge HE-7 (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"
    - "image characterization and classification by physical complexity"
    - "compression based investigation of the dynamical properties of cellular automata and other systems"
    - "classification of random boolean networks"

Connected through hyperedge HE-8 (induced by source paper "two dimensional kolmogorov complexity and validation of the coding theorem method by compressibility"):
  with members:
    - "correspondence and independence of numerical evaluations of algorithmic information measures"
    - "calculating kolmogorov complexity from the output frequency distributions of small turing machines"
    - "compression based investigation of the dynamical properties of cellular automata and other systems"
    - "towards a stable definition of kolmogorov chaitin complexity"
    - "most programs stop quickly or never halt"

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)