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: "integrated pre processing for bayesian nonlinear system identification with gaussian processes"
  Abstract: We introduce GP-FNARX: a new model for nonlinear system identification based on a nonlinear autoregressive exogenous model (NARX) with filtered regressors (F) where the nonlinear regression problem is tackled using sparse Gaussian processes (GP). We integrate data pre-processing with system identification into a fully automated procedure that goes from raw data to an identified model. Both pre-processing parameters and GP hyper-parameters are tuned by maximizing the marginal likelihood of the probabilistic model. We obtain a Bayesian model of the system's dynamics which is able to report its uncertainty in regions where the data is scarce. The automated approach, the modeling of uncertainty and its relatively low computational cost make of GP-FNARX a good candidate for applications in robotics and adaptive control.

Connected through hyperedge HE-1 (induced by source paper "temporal pattern attention for multivariate time series forecasting"):
  with members:
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "effective approaches to attention based neural machine translation"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"

Connected through hyperedge HE-2 (induced by source paper "a dual stage attention based recurrent neural network for time series prediction"):
  with members:
    - "adaptive feature abstraction for translating video to text"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural machine translation by jointly learning to align and translate"
    - "deep visual semantic alignments for generating image descriptions"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"
    - "speech recognition with deep recurrent neural networks"

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)