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: "short note on the behavior of recurrent neural network for noisy dynamical system"
  Abstract: The behavior of recurrent neural network for the data-driven simulation of noisy dynamical systems is studied by training a set of Long Short-Term Memory Networks (LSTM) on the Mackey-Glass time series with a wide range of noise level. It is found that, as the training noise becomes larger, LSTM learns to depend more on its autonomous dynamics than the noisy input data. As a result, LSTM trained on noisy data becomes less susceptible to the perturbation in the data, but has a longer relaxation timescale. On the other hand, when trained on noiseless data, LSTM becomes extremely sensitive to a small perturbation, but is able to adjusts to the changes in the input data.

Connected through hyperedge HE-1 (induced by source paper "variational inference formulation for a model free simulation of a dynamical system with unknown parameters by a recurrent neural network"):
  with members:
    - "learning functionals via lstm neural networks for predicting vessel dynamics in extreme sea states"
    - "disentangled sequential autoencoder"
    - "unsupervised learning of disentangled and interpretable representations from sequential data"
    - "sgdr stochastic gradient descent with restarts"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-2 (induced by source paper "data driven reconstruction of nonlinear dynamics from sparse observation"):
  with members:
    - "brits bidirectional recurrent imputation for time series"
    - "echo state networks are universal"
    - "using a reservoir computer to learn chaotic attractors with applications to chaos synchronization and cryptography"
    - "directly modeling missing data in sequences with rnns improved classification of clinical time series"
    - "recurrent neural networks for multivariate time series with missing values"
    - "deep learning in neural networks"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-3 (induced by source paper "a non intrusive reduced order modeling framework for quasi geostrophic turbulence"):
  with members:
    - "hybrid forecasting of chaotic processes using machine learning in conjunction with a knowledge based model"
    - "dr rnn a deep residual recurrent neural network for model reduction"
    - "deep learning for time series analysis"
    - "efficient construction of local parametric reduced order models using machine learning techniques"
    - "adam a method for stochastic optimization"

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.NE(Neural and Evolutionary Computing)