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: "commonsense reasoning commonsense knowledge and the sp theory of intelligence"
  Abstract: Commonsense reasoning (CSR) and commonsense knowledge (CSK) (together abbreviated as CSRK) are areas of study concerned with problems which are trivially easy for adults but which are challenging for artificial systems. This paper describes how the SP System -- meaning the "SP Theory of Intelligence" and its realisation in the "SP Computer Model" -- has strengths and potential in several aspects of CSRK. Some shortcomings of the system in that area may be overcome with planned future developments. A particular strength of the SP System is that it shows promise as an overarching theory for four areas of relative success with CSRK problems -- described by other authors -- which have been developed without any integrative theory. How the SP System may help to solve four other kinds of CSRK problem is described: 1) how the strength of evidence for a murder may be influenced by the level of lighting of the murder as it was witnessed; 2) how people may arrive at the commonly-accepted interpretation of phrases like "water bird"; 3) the interpretation of the horse's head scene in "The Godfather" film; and how the SP System may help to resolve the reference of an ambiguous pronoun in sentences in the format of a 'Winograd schema'. Also described is why a fifth CSRK problem -- modelling how a chef may crack an egg into a bowl -- is beyond the capabilities of the SP System as it is now and how those deficiencies may be overcome via planned developments of the system.

Connected through hyperedge HE-1 (induced by source paper "introduction to the sp theory of intelligence"):
  with members:
    - "solutions to problems with deep learning"
    - "software engineering and the sp theory of intelligence"
    - "information compression multiple alignment and the representation and processing of knowledge in the brain"
    - "the sp theory of intelligence distinctive features and advantages"
    - "deep learning in neural networks"
    - "the sp theory of intelligence its distinctive features and advantages"
    - "application of the sp theory of intelligence to the understanding of natural vision and the development of computer vision"
    - "big data and the sp theory of intelligence"

Connected through hyperedge HE-2 (induced by source paper "interpreting winograd schemas via the sp theory of intelligence and its realisation in the sp computer model"):
  with members:
    - "introduction to the sp theory of intelligence"
    - "mathematics as information compression via the matching and unification of patterns"
    - "solutions to problems with deep learning"
    - "software engineering and the sp theory of intelligence"
    - "information compression multiple alignment and the representation and processing of knowledge in the brain"
    - "the sp theory of intelligence distinctive features and advantages"
    - "deep learning in neural networks"
    - "the sp theory of intelligence its distinctive features and advantages"

Connected through hyperedge HE-3 (induced by source paper "solutions to problems with deep learning"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "deep learning a critical appraisal"
    - "deep reinforcement learning that matters"
    - "overcoming catastrophic forgetting in neural networks"
    - "information compression multiple alignment and the representation and processing of knowledge in the brain"
    - "the sp theory of intelligence distinctive features and advantages"
    - "deep learning in neural networks"
    - "the sp theory of intelligence its distinctive features and advantages"

Connected through hyperedge HE-4 (induced by source paper "dual approach for object tracking based on optical flow and swarm intelligence"):
  with members:
    - "deep learning a critical appraisal"
    - "solutions to problems with deep learning"
    - "crest convolutional residual learning for visual tracking"
    - "long term visual object tracking benchmark"
    - "the sp theory of intelligence distinctive features and advantages"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "the sp theory of intelligence its distinctive features and advantages"
    - "on the fly learning in a perpetual learning machine"

Connected through hyperedge HE-5 (induced by source paper "development of the sp machine"):
  with members:
    - "introduction to the sp theory of intelligence"
    - "solutions to problems with deep learning"
    - "software engineering and the sp theory of intelligence"
    - "overcoming catastrophic forgetting in neural networks"
    - "information compression multiple alignment and the representation and processing of knowledge in the brain"
    - "the sp theory of intelligence distinctive features and advantages"
    - "deep learning in neural networks"
    - "the sp theory of intelligence its distinctive features and advantages"

Connected through hyperedge HE-6 (induced by source paper "software engineering and the sp theory of intelligence"):
  with members:
    - "introduction to the sp theory of intelligence"
    - "overcoming catastrophic forgetting in neural networks"
    - "the sp theory of intelligence distinctive features and advantages"
    - "the sp theory of intelligence its distinctive features and advantages"
    - "application of the sp theory of intelligence to the understanding of natural vision and the development of computer vision"
    - "big data and the sp theory of intelligence"
    - "autonomous robots and the sp theory of intelligence"
    - "the sp theory of intelligence benefits and applications"

Connected through hyperedge HE-7 (induced by source paper "the sp theory of intelligence as a foundation for the development of a general human level thinking machine"):
  with members:
    - "information compression multiple alignment and the representation and processing of knowledge in the brain"
    - "the sp theory of intelligence its distinctive features and advantages"
    - "application of the sp theory of intelligence to the understanding of natural vision and the development of computer vision"
    - "big data and the sp theory of intelligence"
    - "autonomous robots and the sp theory of intelligence"
    - "information compression intelligence computing and mathematics"
    - "towards an intelligent database system founded on the sp theory of computing and cognition"
    - "medical diagnosis as pattern recognition 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)