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: "solutions to problems with deep learning"
  Abstract: Despite the several successes of deep learning systems, there are concerns about their limitations, discussed most recently by Gary Marcus. This paper discusses Marcus's concerns and some others, together with solutions to several of these problems provided by the "P theory of intelligence" and its realisation in the "SP computer model". The main advantages of the SP system are: relatively small requirements for data and the ability to learn from a single experience; the ability to model both hierarchical and non-hierarchical structures; strengths in several kinds of reasoning, including `commonsense' reasoning; transparency in the representation of knowledge, and the provision of an audit trail for all processing; the likelihood that the SP system could not be fooled into bizarre or eccentric recognition of stimuli, as deep learning systems can be; the SP system provides a robust solution to the problem of `catastrophic forgetting' in deep learning systems; the SP system provides a theoretically-coherent solution to the problems of correcting over- and under-generalisations in learning, and learning correct structures despite errors in data; unlike most research on deep learning, the SP programme of research draws extensively on research on human learning, perception, and cognition; and the SP programme of research has an overarching theory, supported by evidence, something that is largely missing from research on deep learning. In general, the SP system provides a much firmer foundation than deep learning for the development of artificial general intelligence.

Connected through hyperedge HE-1 (induced by source paper "introduction to the sp theory of intelligence"):
  with members:
    - "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"
    - "commonsense reasoning commonsense knowledge and the sp theory of intelligence"
    - "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"
    - "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"
    - "commonsense reasoning commonsense knowledge and the sp theory of intelligence"
    - "deep learning in neural networks"
    - "the sp theory of intelligence its distinctive features and advantages"

Connected through hyperedge HE-3 (induced by source paper "dual approach for object tracking based on optical flow and swarm intelligence"):
  with members:
    - "deep learning a critical appraisal"
    - "crest convolutional residual learning for visual tracking"
    - "long term visual object tracking benchmark"
    - "the sp theory of intelligence distinctive features and advantages"
    - "commonsense reasoning commonsense knowledge and the sp theory of intelligence"
    - "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-4 (induced by source paper "development of the sp machine"):
  with members:
    - "introduction to the sp theory of intelligence"
    - "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"
    - "commonsense reasoning commonsense knowledge and the sp theory of intelligence"
    - "deep learning in neural networks"
    - "the sp theory of intelligence its distinctive features and advantages"

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.LG(Machine Learning)