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: "a decomposition method for global evaluation of shannon entropy and local estimations of algorithmic complexity"
  Abstract: We investigate the properties of a Block Decomposition Method (BDM), which extends the power of a Coding Theorem Method (CTM) that approximates local estimations of algorithmic complexity based upon Solomonoff-Levin's theory of algorithmic probability providing a closer connection to algorithmic complexity than previous attempts based on statistical regularities e.g. as spotted by some popular lossless compression schemes. The strategy behind BDM is to find small computer programs that produce the components of a larger, decomposed object. The set of short computer programs can then be artfully arranged in sequence so as to produce the original object and to estimate an upper bound on the length of the shortest computer program that produces said original object. We show that the method provides efficient estimations of algorithmic complexity but that it performs like Shannon entropy when it loses accuracy. We estimate errors and study the behaviour of BDM for different boundary conditions, all of which are compared and assessed in detail. The measure may be adapted for use with more multi-dimensional objects than strings, objects such as arrays and tensors. To test the measure we demonstrate the power of CTM on low algorithmic-randomness objects that are assigned maximal entropy (e.g. $\pi$) but whose numerical approximations are closer to the theoretical low algorithmic-randomness expectation. We also test the measure on larger objects including dual, isomorphic and cospectral graphs for which we know that algorithmic randomness is low. We also release implementations of the methods in most major programming languages---Wolfram Language (Mathematica), Matlab, R, Perl, Python, Pascal, C++, and Haskell---and a free online algorithmic complexity calculator.

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"
    - "coding theorem like behaviour and emergence of the universal distribution from resource bounded algorithmic probability"
    - "algorithmically probable mutations reproduce aspects of evolution such as convergence rate genetic memory and modularity"
    - "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 "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"
    - "turing patterns with turing machines emergence and low level structure formation"
    - "on the kolmogorov chaitin complexity for short sequences"

Connected through hyperedge HE-3 (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"
    - "coding theorem like behaviour and emergence of the universal distribution from resource bounded algorithmic probability"
    - "low algorithmic complexity entropy deceiving graphs"
    - "algorithmically probable mutations reproduce aspects of evolution such as convergence rate genetic memory and modularity"
    - "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-4 (induced by source paper "parameter free network sparsification and data reduction by minimal algorithmic information loss"):
  with members:
    - "coding theorem like behaviour and emergence of the universal distribution from resource bounded algorithmic probability"
    - "low algorithmic complexity entropy deceiving graphs"
    - "algorithmic data analytics small data matters and correlation versus causation"
    - "a model of internet topology using k shell decomposition"
    - "the google similarity distance"
    - "the similarity metric"

Connected through hyperedge HE-5 (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"
    - "coding theorem like behaviour and emergence of the universal distribution from resource bounded algorithmic probability"
    - "low algorithmic complexity entropy deceiving graphs"
    - "algorithmic data analytics small data matters and correlation versus causation"
    - "shannon information and kolmogorov complexity"

Connected through hyperedge HE-6 (induced by source paper "symmetry and algorithmic complexity of polyominoes and polyhedral graphs"):
  with members:
    - "low algorithmic complexity entropy deceiving graphs"
    - "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"
    - "structure induction by lossless graph compression"

Connected through hyperedge HE-7 (induced by source paper "algorithmically probable mutations reproduce aspects of evolution such as convergence rate genetic memory and modularity"):
  with members:
    - "coding theorem like behaviour and emergence of the universal distribution from resource bounded algorithmic probability"
    - "low algorithmic complexity entropy deceiving graphs"
    - "undecidability and irreducibility conditions for open ended evolution and emergence"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "low algorithmic complexity entropy deceiving graphs"):
  with members:
    - "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.IT(Information Theory)