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: "algorithmically probable mutations reproduce aspects of evolution such as convergence rate genetic memory and modularity"
  Abstract: Natural selection explains how life has evolved over millions of years from more primitive forms. The speed at which this happens, however, has sometimes defied formal explanations when based on random (uniformly distributed) mutations. Here we investigate the application of a simplicity bias based on a natural but algorithmic distribution of mutations (no recombination) in various examples, particularly binary matrices in order to compare evolutionary convergence rates. Results both on synthetic and on small biological examples indicate an accelerated rate when mutations are not statistical uniform but \textit{algorithmic uniform}. We show that algorithmic distributions can evolve modularity and genetic memory by preservation of structures when they first occur sometimes leading to an accelerated production of diversity but also population extinctions, possibly explaining naturally occurring phenomena such as diversity explosions (e.g. the Cambrian) and massive extinctions (e.g. the End Triassic) whose causes are currently a cause for debate. The natural approach introduced here appears to be a better approximation to biological evolution than models based exclusively upon random uniform mutations, and it also approaches a formal version of open-ended evolution based on previous formal results. These results validate some suggestions in the direction that computation may be an equally important driver of evolution. We also show that inducing the method on problems of optimization, such as genetic algorithms, has the potential to accelerate convergence of artificial evolutionary algorithms.

Connected through hyperedge HE-1 (induced by source paper "algorithmic networks central time to trigger expected emergent open endedness"):
  with members:
    - "self referential basis of undecidable dynamics from the liar paradox and the halting problem to the edge of chaos"
    - "quantifying synergistic information using intermediate stochastic variables"
    - "the paradox of computability and a recursive relative version of the busy beaver function"
    - "computing halting probabilities from other halting probabilities"
    - "undecidability and irreducibility conditions for open ended evolution and emergence"
    - "relativizing an incompressible number and an incompressible function through subrecursive extensions of turing machines"
    - "on multiaspect graphs"
    - "formal definitions of unbounded evolution and innovation reveal universal mechanisms for open ended evolution in dynamical systems"

Connected through hyperedge HE-2 (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"
    - "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-3 (induced by source paper "learning the undecidable from networked systems"):
  with members:
    - "the paradox of computability and a recursive relative version of the busy beaver function"
    - "undecidability and irreducibility conditions for open ended evolution and emergence"
    - "time centrality in dynamic complex networks"
    - "quantifying synergistic mutual information"

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"
    - "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"
    - "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 "coding theorem like behaviour and emergence of the universal distribution from resource bounded algorithmic probability"):
  with members:
    - "computing by nowhere increasing complexity"
    - "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"
    - "kolmogorov complexity and solovay functions"
    - "towards a stable definition of kolmogorov chaitin complexity"
    - "optimal ordered problem solver"

Connected through hyperedge HE-6 (induced by source paper "slime mould the fundamental mechanisms of cognition"):
  with members:
    - "low algorithmic complexity entropy deceiving graphs"
    - "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"
    - "on creativity of slime mould"
    - "physarum machine implementation of a kolmogorov uspensky machine on a biological substrate"

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.NE(Neural and Evolutionary Computing)