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: "knowledge transfer between artificial intelligence systems"
  Abstract: We consider the fundamental question: how a legacy "student" Artificial Intelligent (AI) system could learn from a legacy "teacher" AI system or a human expert without complete re-training and, most importantly, without requiring significant computational resources. Here "learning" is understood as an ability of one system to mimic responses of the other and vice-versa. We call such learning an Artificial Intelligence knowledge transfer. We show that if internal variables of the "student" Artificial Intelligent system have the structure of an $n$-dimensional topological vector space and $n$ is sufficiently high then, with probability close to one, the required knowledge transfer can be implemented by simple cascades of linear functionals. In particular, for $n$ sufficiently large, with probability close to one, the "student" system can successfully and non-iteratively learn $k\ll n$ new examples from the "teacher" (or correct the same number of mistakes) at the cost of two additional inner products. The concept is illustrated with an example of knowledge transfer from a pre-trained convolutional neural network to a simple linear classifier with HOG features.

Connected through hyperedge HE-1 (induced by source paper "the unreasonable effectiveness of small neural ensembles in high dimensional brain"):
  with members:
    - "correction of ai systems by linear discriminants probabilistic foundations"
    - "augmented artificial intelligence"
    - "blessing of dimensionality mathematical foundations of the statistical physics of data"
    - "augmented artificial intelligence a conceptual framework"
    - "stochastic separation theorems"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "approximation with random bases"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "correction of ai systems by linear discriminants probabilistic foundations"):
  with members:
    - "augmented artificial intelligence"
    - "blessing of dimensionality mathematical foundations of the statistical physics of data"
    - "augmented artificial intelligence a conceptual framework"
    - "how deep should be the depth of convolutional neural networks a backyard dog case study"
    - "stochastic separation theorems"
    - "stochastic configuration networks fundamentals and algorithms"
    - "neurogenesis deep learning extending deep networks to accommodate new classes"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-3 (induced by source paper "blessing of dimensionality mathematical foundations of the statistical physics of data"):
  with members:
    - "stochastic separation theorems"
    - "stochastic configuration networks fundamentals and algorithms"
    - "piece wise quadratic approximations of arbitrary error functions for fast and robust machine learning"
    - "approximation with random bases"
    - "caffe convolutional architecture for fast feature embedding"
    - "data complexity measured by principal graphs"
    - "the more the merrier the blessing of dimensionality for learning large gaussian mixtures"
    - "principal manifolds and graphs in practice from molecular biology to dynamical 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)