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: "nonlinear systems identification using deep dynamic neural networks"
  Abstract: Neural networks are known to be effective function approximators. Recently, deep neural networks have proven to be very effective in pattern recognition, classification tasks and human-level control to model highly nonlinear realworld systems. This paper investigates the effectiveness of deep neural networks in the modeling of dynamical systems with complex behavior. Three deep neural network structures are trained on sequential data, and we investigate the effectiveness of these networks in modeling associated characteristics of the underlying dynamical systems. We carry out similar evaluations on select publicly available system identification datasets. We demonstrate that deep neural networks are effective model estimators from input-output data

Connected through hyperedge HE-1 (induced by source paper "probabilistic forecasting of sensory data with generative adversarial networks forgan"):
  with members:
    - "phaselink a deep learning approach to seismic phase association"
    - "predicting inpatient discharge prioritization with electronic health records"
    - "deepdownscale a deep learning strategy for high resolution weather forecast"
    - "anomaly detection with generative adversarial networks for multivariate time series"
    - "voice impersonation using generative adversarial networks"
    - "cred a deep residual network of convolutional and recurrent units for earthquake signal detection"
    - "svsgan singing voice separation via generative adversarial network"
    - "adversarial learning for neural dialogue generation"

Connected through hyperedge HE-2 (induced by source paper "neural learning trajectory tracking control of flexible joint robot manipulators with unknown dynamics"):
  with members:
    - "industrial robot trajectory tracking using multi layer neural networks trained by iterative learning control"
    - "deep neural networks for improved impromptu trajectory tracking of quadrotors"

Connected through hyperedge HE-3 (induced by source paper "detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding"):
  with members:
    - "recent trends in deep learning based natural language processing"
    - "text classification improved by integrating bidirectional lstm with two dimensional max pooling"
    - "lstm based encoder decoder for multi sensor anomaly detection"
    - "deep learning in neural networks"
    - "evaluating real time anomaly detection algorithms the numenta anomaly benchmark"
    - "long short term memory based recurrent neural network architectures for large vocabulary speech recognition"
    - "sequence to sequence learning with neural networks"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "models and framework for adversarial attacks on complex adaptive systems"):
  with members:
    - "deep reinforcement learning an overview"
    - "vulnerability of deep reinforcement learning to policy induction attacks"
    - "a game theoretic model for analysis and design of self organization mechanisms in iot"
    - "cyber physical attacks on uas networks challenges and open research problems"
    - "practical black box attacks against machine learning"

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)