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 "ab initio algorithmic causal deconvolution of intertwined programs and networks by generative mechanism"):
  Abstract: To extract and learn representations leading to generative mechanisms from data, especially without making arbitrary decisions and biased assumptions, is a central challenge in most areas of scientific research particularly in connection to current major limitations of influential topics and methods of machine and deep learning as they have often lost sight of the model component. Complex data is usually produced by interacting sources with different mechanisms. Here we introduce a parameter-free model-based approach, based upon the seminal concept of Algorithmic Probability, that decomposes an observation and signal into its most likely algorithmic generative mechanisms. Our methods use a causal calculus to infer model representations. We demonstrate the method ability to distinguish interacting mechanisms and deconvolve them, regardless of whether the objects produce strings, space-time evolution diagrams, images or networks. We numerically test and evaluate our method and find that it can disentangle observations from discrete dynamic systems, random and complex networks. We think that these causal inference techniques can contribute as key pieces of information for estimations of probability distributions complementing other more statistical-oriented techniques that otherwise lack model inference capabilities.

Includes members:
  Member "parameter free network sparsification and data reduction by minimal algorithmic information loss":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an algorithmic refinement of maxent induces a thermodynamic like behaviour in the reprogrammability of generative mechanisms")
  Member "coding theorem like behaviour and emergence of the universal distribution from resource bounded algorithmic probability":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "the thermodynamics of network coding and an algorithmic refinement of the principle of maximum entropy")
  Member "algorithmically probable mutations reproduce aspects of evolution such as convergence rate genetic memory and modularity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "algorithmic probability guided supervised machine learning on non differentiable spaces")
  Member "on the necessity of complexity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "complexity fits the fittest")
  Member "two dimensional kolmogorov complexity and validation of the coding theorem method by compressibility":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "algorithmically probable mutations reproduce aspects of evolution such as convergence rate genetic memory and modularity")
  Member "nonnegative decomposition of multivariate information":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an operational information decomposition via synergistic disclosure")

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.AI(Artificial Intelligence)