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: "information compression multiple alignment and the representation and processing of knowledge in the brain"
  Abstract: The “SP theory of intelligence”, with its realisation in the “SP computer model”, aims to simplify and integrate observations and concepts across artificial intelligence, mainstream computing, mathematics, and human perception and cognition, with information compression as a unifying theme. This paper describes how abstract structures and processes in the theory may be realised in terms of neurons, their interconnections, and the transmission of signals between neurons. This part of the SP theory -- “SP-neural” -- is a tentative and partial model for the representation and processing of knowledge in the brain. Empirical support for the SP theory -- outlined in the paper -- provides indirect support for SP-neural. In the abstract part of the SP theory (SP-abstract), all kinds of knowledge are represented with “patterns”, where a pattern is an array of atomic symbols in one or two dimensions. In SP-neural, the concept of a ‘pattern’ is realised as an array of neurons called a “pattern assembly”, similar to Hebb's concept of a ‘cell assembly’ but with important differences. Central to the processing of information in SP-abstract is information compression via the matching and unification of patterns (ICMUP) and, more specifically, information compression via the powerful concept of “multiple alignment”, borrowed and adapted from bioinformatics. Processes such as pattern recognition, reasoning and problem solving are achieved via the building of multiple alignments, while unsupervised learning is achieved by creating patterns from sensory information and also by creating patterns from multiple alignments in which there is a partial match between one pattern and another. It is envisaged that, in SP-neural, short-lived neural structures equivalent to multiple alignments will be created via an inter-play of excitatory and inhibitory neural signals. It is also envisaged that unsupervised learning will be achieved by the creation of pattern assemblies from sensory information and from the neural equivalents of multiple alignments, much as in the non-neural SP theory -- and significantly different from the `Hebbian' kinds of learning which are widely used in the kinds of artificial neural network that are popular in computer science. The paper discusses several associated issues, with relevant empirical evidence.

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"
    - "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"
    - "solutions to problems with deep learning"
    - "software engineering and the sp theory of intelligence"
    - "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 "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"
    - "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-4 (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"
    - "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"

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"
    - "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-6 (induced by source paper "commonsense reasoning commonsense knowledge and the sp theory of intelligence"):
  with members:
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "the sp theory of intelligence distinctive features and advantages"
    - "the sp theory of intelligence as a foundation for the development of a general human level thinking machine"
    - "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 an overview"

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:
    - "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"
    - "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)