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: "probabilistic reasoning as information compression by multiple alignment unification and search an introduction and overview"
  Abstract: This article introduces the idea that probabilistic reasoning (PR) may be understood as "information compression by multiple alignment, unification and search" (ICMAUS). In this context, multiple alignment has a meaning which is similar to but distinct from its meaning in bio-informatics, while unification means a simple merging of matching patterns, a meaning which is related to but simpler than the meaning of that term in logic. #R##N#A software model, SP61, has been developed for the discovery and formation of 'good' multiple alignments, evaluated in terms of information compression. The model is described in outline. #R##N#Using examples from the SP61 model, this article describes in outline how the ICMAUS framework can model various kinds of PR including: PR in best-match pattern recognition and information retrieval; one-step 'deductive' and 'abductive' PR; inheritance of attributes in a class hierarchy; chains of reasoning (probabilistic decision networks and decision trees, and PR with 'rules'); geometric analogy problems; nonmonotonic reasoning and reasoning with default values; modelling the function of a Bayesian network.

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"
    - "unsupervised grammar induction in a framework of information compression by multiple alignment unification and search"
    - "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"
    - "unsupervised grammar induction in a framework of information compression by multiple alignment unification and search"
    - "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 "unifying computing and cognition the sp theory and its applications"):
  with members:
    - "syntax parsing and production of natural language in a framework of information compression by multiple alignment unification and search"
    - "computing as information compression by multiple alignment unification and search"
    - "bootstrapping structure into language alignment based learning"

Connected through hyperedge HE-4 (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"
    - "unsupervised grammar induction in a framework of information compression by multiple alignment unification and search"
    - "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-5 (induced by source paper "unsupervised learning 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"
    - "computing as information compression by multiple alignment unification and search"

Connected through hyperedge HE-6 (induced by source paper "an alternative to rdf based languages for the representation and processing of ontologies in the semantic web"):
  with members:
    - "syntax parsing and production of natural language 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-7 (induced by source paper "syntax parsing and production of natural language in a framework of information compression by multiple alignment unification and search"):
  with members:
    - "computing as information compression by multiple alignment unification and search"

Connected through hyperedge HE-8 (induced by source paper "information compression by multiple alignment unification and search as a unifying principle in computing and cognition"):
  with members:
    - "unsupervised learning in a framework of 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)