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: "coding theorem like behaviour and emergence of the universal distribution from resource bounded algorithmic probability"
  Abstract: Previously referred to as `miraculous' in the scientific literature because of its powerful properties and its wide application as optimal solution to the problem of induction/inference, (approximations to) Algorithmic Probability (AP) and the associated Universal Distribution are (or should be) of the greatest importance in science. Here we investigate the emergence, the rates of emergence and convergence, and the Coding-theorem like behaviour of AP in Turing-subuniversal models of computation. We investigate empirical distributions of computing models in the Chomsky hierarchy. We introduce measures of algorithmic probability and algorithmic complexity based upon resource-bounded computation, in contrast to previously thoroughly investigated distributions produced from the output distribution of Turing machines. This approach allows for numerical approximations to algorithmic (Kolmogorov-Chaitin) complexity-based estimations at each of the levels of a computational hierarchy. We demonstrate that all these estimations are correlated in rank and that they converge both in rank and values as a function of computational power, despite fundamental differences between computational models. In the context of natural processes that operate below the Turing universal level because of finite resources and physical degradation, the investigation of natural biases stemming from algorithmic rules may shed light on the distribution of outcomes. We show that up to 60\% of the simplicity/complexity bias in distributions produced even by the weakest of the computational models can be accounted for by Algorithmic Probability in its approximation to the Universal Distribution.

Connected through hyperedge HE-1 (induced by source paper "algorithmic probability guided supervised machine learning on non differentiable spaces"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "compression is comprehension and the unreasonable effectiveness of digital computation in the natural world"
    - "algorithmically probable mutations reproduce aspects of evolution such as convergence rate genetic memory and modularity"
    - "a decomposition method for global evaluation of shannon entropy and local estimations of algorithmic complexity"
    - "adam a method for stochastic optimization"
    - "calculating kolmogorov complexity from the output frequency distributions of small turing machines"
    - "on the algorithmic nature of the world"

Connected through hyperedge HE-2 (induced by source paper "the thermodynamics of network coding and an algorithmic refinement of the principle of maximum entropy"):
  with members:
    - "low algorithmic complexity entropy deceiving graphs"
    - "a computable measure of algorithmic probability by finite approximations with an application to integer sequences"
    - "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"
    - "shannon information and kolmogorov complexity"

Connected through hyperedge HE-3 (induced by source paper "compression is comprehension and the unreasonable effectiveness of digital computation in the natural world"):
  with members:
    - "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"
    - "on the kolmogorov chaitin complexity for short sequences"

Connected through hyperedge HE-4 (induced by source paper "ab initio algorithmic causal deconvolution of intertwined programs and networks by generative mechanism"):
  with members:
    - "parameter free network sparsification and data reduction by minimal algorithmic information loss"
    - "low algorithmic complexity entropy deceiving graphs"
    - "algorithmically probable mutations reproduce aspects of evolution such as convergence rate genetic memory and modularity"
    - "a decomposition method for global evaluation of shannon entropy and local estimations of algorithmic complexity"
    - "on the necessity of complexity"
    - "two dimensional kolmogorov complexity and validation of the coding theorem method by compressibility"
    - "nonnegative decomposition of multivariate information"
    - "the generalized universal law of generalization"

Connected through hyperedge HE-5 (induced by source paper "parameter free network sparsification and data reduction by minimal algorithmic information loss"):
  with members:
    - "low algorithmic complexity entropy deceiving graphs"
    - "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"
    - "a model of internet topology using k shell decomposition"
    - "the google similarity distance"
    - "the similarity metric"

Connected through hyperedge HE-6 (induced by source paper "data dimension reduction and network sparsification based on minimal algorithmic information loss"):
  with members:
    - "low algorithmic complexity entropy deceiving graphs"
    - "correspondence and independence of numerical evaluations of algorithmic information measures"
    - "graph sparsification by effective resistances"
    - "a model of internet topology using k shell decomposition"
    - "the google similarity distance"
    - "the similarity metric"

Connected through hyperedge HE-7 (induced by source paper "an algorithmic refinement of maxent induces a thermodynamic like behaviour in the reprogrammability of generative mechanisms"):
  with members:
    - "parameter free network sparsification and data reduction by minimal algorithmic information loss"
    - "low algorithmic complexity entropy deceiving graphs"
    - "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"
    - "shannon information and kolmogorov complexity"

Connected through hyperedge HE-8 (induced by source paper "algorithmically probable mutations reproduce aspects of evolution such as convergence rate genetic memory and modularity"):
  with members:
    - "low algorithmic complexity entropy deceiving graphs"
    - "undecidability and irreducibility conditions for open ended evolution and emergence"
    - "a decomposition method for global evaluation of shannon entropy and local estimations of algorithmic complexity"
    - "the limits of decidable states on open ended evolution and emergence"
    - "a computable measure of algorithmic probability by finite approximations with an application to integer sequences"
    - "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"
    - "two dimensional kolmogorov complexity and validation of the coding theorem method by compressibility"

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)