human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "coding theorem like behaviour and emergence of the universal distribution from resource bounded algorithmic probability":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "algorithmic probability guided supervised machine learning on non differentiable spaces")
  Member "low algorithmic complexity entropy deceiving graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the thermodynamics of network coding and an algorithmic refinement of the principle of maximum entropy")
  Member "undecidability and irreducibility conditions for open ended evolution and emergence":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "algorithmic networks central time to trigger expected emergent open endedness")
  Member "a decomposition method for global evaluation of shannon entropy and local estimations of algorithmic complexity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "algorithmic probability guided supervised machine learning on non differentiable spaces")
  Member "the limits of decidable states on open ended evolution and emergence":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "relativizing an incompressible number and an incompressible function through subrecursive extensions of turing machines")
  Member "a computable measure of algorithmic probability by finite approximations with an application to integer sequences":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "how incomputable is kolmogorov complexity")
  Member "correlation of automorphism group size and topological properties with program size complexity evaluations of graphs and complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the thermodynamics of network coding and an algorithmic refinement of the principle of maximum entropy")
  Member "calculating kolmogorov complexity from the output frequency distributions of small turing machines":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "how incomputable is kolmogorov complexity")

Question: Please tell me which class the source paper 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)