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: "unsupervised grammar induction in a framework of information compression by multiple alignment unification and search"
  Abstract: This paper describes a novel approach to grammar induction that has been developed within a framework designed to integrate learning with other aspects of computing, AI, mathematics and logic. This framework, called "information compression by multiple alignment, unification and search" (ICMAUS), is founded on principles of Minimum Length Encoding pioneered by Solomonoff and others. Most of the paper describes SP70, a computer model of the ICMAUS framework that incorporates processes for unsupervised learning of grammars. An example is presented to show how the model can infer a plausible grammar from appropriate input. Limitations of the current model and how they may be overcome are briefly discussed.

Connected through hyperedge HE-1 (induced by source paper "towards an intelligent database system founded on the sp theory of computing and cognition"):
  with members:
    - "unifying computing and cognition the sp theory and its applications"
    - "syntax parsing and production of natural language in a framework of information compression by multiple alignment unification and search"
    - "probabilistic reasoning as information compression by multiple alignment unification and search an introduction and overview"
    - "unsupervised learning in a framework of information compression by multiple alignment unification and search"
    - "an alternative to rdf based languages for the representation and processing of ontologies in the semantic web"
    - "computing as information compression by multiple alignment unification and search"

Connected through hyperedge HE-2 (induced by source paper "medical diagnosis as pattern recognition in a framework of information compression by multiple alignment unification and search"):
  with members:
    - "syntax parsing and production of natural language in a framework of information compression by multiple alignment unification and search"
    - "probabilistic reasoning as information compression by multiple alignment unification and search an introduction and overview"
    - "unsupervised learning in a framework of information compression by multiple alignment unification and search"
    - "computing as information compression by multiple alignment unification and search"

Connected through hyperedge HE-3 (induced by source paper "neural realisation of the sp theory cell assemblies revisited"):
  with members:
    - "syntax parsing and production of natural language in a framework of information compression by multiple alignment unification and search"
    - "probabilistic reasoning as information compression by multiple alignment unification and search an introduction and overview"
    - "unsupervised learning in a framework of information compression by multiple alignment unification and search"
    - "computing as information compression by multiple alignment unification and search"

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.AI(Artificial Intelligence)