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: "the sp theory of intelligence an overview"
  Abstract: This article is an overview of the SP theory of intelligence, which aims to simplify and integrate concepts across artificial intelligence, mainstream computing and human perception and cognition, with information compression as a unifying theme. It is conceived of as a brain-like system that receives "New" information and stores some or all of it in compressed form as "Old" information; and it is realised in the form of a computer model, a first version of the SP machine. The matching and unification of patterns and the concept of multiple alignment are central ideas. Using heuristic techniques, the system builds multiple alignments that are "good" in terms of information compression. For each multiple alignment, probabilities may be calculated for associated inferences. Unsupervised learning is done by deriving new structures from partial matches between patterns and via heuristic search for sets of structures that are "good" in terms of information compression. These are normally ones that people judge to be "natural", in accordance with the "DONSVIC" principle—the discovery of natural structures via information compression. The SP theory provides an interpretation for concepts and phenomena in several other areas, including "computing", aspects of mathematics and logic, the representation of knowledge, natural language processing, pattern recognition, several kinds of reasoning, information storage and retrieval, planning and problem solving, information compression, neuroscience and human perception and cognition. Examples include the parsing and production of language with discontinuous dependencies in syntax, pattern recognition at multiple levels of abstraction and its integration with part-whole relations, nonmonotonic reasoning and reasoning with default values, reasoning in Bayesian networks, including "explaining away", causal diagnosis, and the solving of a geometric analogy problem.

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"
    - "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"

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"
    - "commonsense reasoning commonsense knowledge and the sp theory of intelligence"
    - "deep learning in neural networks"

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"
    - "commonsense reasoning commonsense knowledge and the sp theory of intelligence"
    - "deep learning in neural networks"

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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "mathematics as information compression via the matching and unification of patterns"):
  with members:
    - "introduction to the sp theory of intelligence"
    - "development of the sp machine"
    - "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"
    - "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-6 (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"
    - "commonsense reasoning commonsense knowledge and the sp theory of intelligence"
    - "deep learning in neural networks"

Connected through hyperedge HE-7 (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"
    - "commonsense reasoning commonsense knowledge and the sp theory of intelligence"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "the sp theory of intelligence distinctive features and advantages"):
  with members:
    - "semantic amodal segmentation"
    - "deep learning in neural networks"
    - "proposal for the creation of a research facility for the development of the sp machine"
    - "text understanding from scratch"
    - "sketch a net that beats humans"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "neural turing machines"
    - "application of the sp theory of intelligence to the understanding of natural vision and the development of computer vision"

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)