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: "augmented artificial intelligence"
  Abstract: All artificial Intelligence (AI) systems make errors. These errors are unexpected, and differ often from the typical human mistakes ("non-human" errors). The AI errors should be corrected without damage of existing skills and, hopefully, avoiding direct human expertise. This talk presents an initial summary report of project taking new and systematic approach to improving the intellectual effectiveness of the individual AI by communities of AIs. We combine some ideas of learning in heterogeneous multiagent systems with new and original mathematical approaches for non-iterative corrections of errors of legacy AI systems.

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"
    - "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"
    - "knowledge transfer between artificial intelligence systems"
    - "approximation with random bases"
    - "deep residual learning for image recognition"

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"
    - "fast construction of correcting ensembles for legacy artificial intelligence systems algorithms and a case study"
    - "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"
    - "stochastic configuration networks fundamentals and algorithms"
    - "game theoretic modeling of driver and vehicle interactions for verification and validation of autonomous vehicle control systems"

Connected through hyperedge HE-3 (induced by source paper "correction of ai systems by linear discriminants probabilistic foundations"):
  with members:
    - "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"
    - "knowledge transfer between artificial intelligence 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)