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 "information compression multiple alignment and the representation and processing of knowledge in the brain"):
  Abstract: The “SP theory of intelligence”, with its realisation in the “SP computer model”, aims to simplify and integrate observations and concepts across artificial intelligence, mainstream computing, mathematics, and human perception and cognition, with information compression as a unifying theme. This paper describes how abstract structures and processes in the theory may be realised in terms of neurons, their interconnections, and the transmission of signals between neurons. This part of the SP theory -- “SP-neural” -- is a tentative and partial model for the representation and processing of knowledge in the brain. Empirical support for the SP theory -- outlined in the paper -- provides indirect support for SP-neural. In the abstract part of the SP theory (SP-abstract), all kinds of knowledge are represented with “patterns”, where a pattern is an array of atomic symbols in one or two dimensions. In SP-neural, the concept of a ‘pattern’ is realised as an array of neurons called a “pattern assembly”, similar to Hebb's concept of a ‘cell assembly’ but with important differences. Central to the processing of information in SP-abstract is information compression via the matching and unification of patterns (ICMUP) and, more specifically, information compression via the powerful concept of “multiple alignment”, borrowed and adapted from bioinformatics. Processes such as pattern recognition, reasoning and problem solving are achieved via the building of multiple alignments, while unsupervised learning is achieved by creating patterns from sensory information and also by creating patterns from multiple alignments in which there is a partial match between one pattern and another. It is envisaged that, in SP-neural, short-lived neural structures equivalent to multiple alignments will be created via an inter-play of excitatory and inhibitory neural signals. It is also envisaged that unsupervised learning will be achieved by the creation of pattern assemblies from sensory information and from the neural equivalents of multiple alignments, much as in the non-neural SP theory -- and significantly different from the `Hebbian' kinds of learning which are widely used in the kinds of artificial neural network that are popular in computer science. The paper discusses several associated issues, with relevant empirical evidence.

Includes members:
  Member "the sp theory of intelligence distinctive features and advantages":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "introduction to the sp theory of intelligence")
  Member "deep learning in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "π nets deep polynomial neural networks")
  Member "proposal for the creation of a research facility for the development of the sp machine":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the sp theory of intelligence distinctive features and advantages")
  Member "the sp theory of intelligence its distinctive features and advantages":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "introduction to the sp theory of intelligence")
  Member "application of the sp theory of intelligence to the understanding of natural vision and the development of computer vision":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "introduction to the sp theory of intelligence")
  Member "big data and the sp theory of intelligence":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "introduction to the sp theory of intelligence")
  Member "autonomous robots and the sp theory of intelligence":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "introduction to the sp theory of intelligence")
  Member "the sp theory of intelligence benefits and applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "introduction to the sp theory of intelligence")

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)