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: "fast construction of correcting ensembles for legacy artificial intelligence systems algorithms and a case study"
  Abstract: Abstract   This paper presents a new approach for constructing simple and computationally efficient improvements of generic Artificial Intelligence (AI) systems, including Multilayer and Deep Learning neural networks. The improvements are small network ensembles added to the existing AI architectures. Theoretical foundations of the approach are based on stochastic separation theorems and the ideas of the concentration of measure. We show that, subject to mild technical assumptions on statistical properties of internal signals in the original AI system, the approach enables fast removal of the AI’s errors with probability close to one on the datasets which may be exponentially large in dimension. The approach is illustrated with numerical examples and a case study of digits recognition in American Sign Language.

Connected through hyperedge HE-1 (induced by source paper "high dimensional brain in a high dimensional world blessing of dimensionality"):
  with members:
    - "the unreasonable effectiveness of small neural ensembles in high dimensional brain"
    - "blessing of dimensionality at the edge"
    - "estimating the effective dimension of large biological datasets using fisher separability analysis"
    - "correction of ai systems by linear discriminants probabilistic foundations"
    - "blessing of dimensionality mathematical foundations of the statistical physics of data"
    - "stochastic separation theorems"
    - "tensor networks for dimensionality reduction and large scale optimization part 2 applications and future perspectives"
    - "approximation with random bases"

Connected through hyperedge HE-2 (induced by source paper "blessing of dimensionality at the edge"):
  with members:
    - "the unreasonable effectiveness of small neural ensembles in high dimensional brain"
    - "estimating the effective dimension of large biological datasets using fisher separability analysis"
    - "correction of ai systems by linear discriminants probabilistic foundations"
    - "augmented artificial intelligence"
    - "blessing of dimensionality mathematical foundations of the statistical physics of data"
    - "stochastic separation theorems"
    - "stochastic configuration networks fundamentals and algorithms"
    - "game theoretic modeling of driver and vehicle interactions for verification and validation of autonomous vehicle control systems"

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)