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: "autonomous robots and the sp theory of intelligence"
  Abstract: This paper is about how the SP theory of intelligence and its realization in the SP machine (both outlined in this paper) may help in the design of the brains of autonomous robots, meaning robots that do not depend on external intelligence or power supplies, are mobile, and have human-like versatility and adaptability in intelligence. This paper addresses three main problems: 1) how to increase the computational and energy efficiency of computers and to reduce their size and weight; 2) how to achieve human-like versatility in intelligence; and 3) likewise for human-like adaptability in intelligence. Regarding the first problem, the SP system has the potential for substantial gains in computational efficiency, with corresponding cuts in energy consumption and the bulkiness of computers: 1) by reducing the size of data to be processed; 2) by exploiting statistical information that the system gathers as an integral part of how it works; and 3) via a new version of Donald Hebb's concept of a cell assembly. Toward human-like versatility in intelligence, the SP system has strengths in unsupervised learning, natural language processing, pattern recognition, information retrieval, several kinds of reasoning, planning, problem solving, and more, with seamless integration among structures and functions. The SP system's strengths in unsupervised learning and other aspects of intelligence may help in achieving human-like adaptability in intelligence via: 1) one-trial learning; 2) learning of natural language; 3) learning to see; 4) building 3-D models of objects and of a robot's surroundings; 5) learning regularities in the workings of a robot and in the robot's environment; 6) exploration and play; 7) learning major skills; and 8) learning via demonstration. Also discussed are how the SP system may process parallel streams of information, generalization of knowledge, correction of over-generalizations, learning from dirty data, how to cut the cost of learning, and reinforcements and motivations.

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"
    - "the sp theory of intelligence benefits and applications"

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)