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: "lie transform based polynomial neural networks for dynamical systems simulation and identification"
  Abstract: In the article, we discuss the architecture of the polynomial neural network that corresponds to the matrix representation of Lie transform. The matrix form of Lie transform is an approximation of general solution for the nonlinear system of ordinary differential equations. Thus, it can be used for simulation and modeling task. On the other hand, one can identify dynamical system from time series data simply by optimization of the coefficient matrices of the Lie transform. Representation of the approach by polynomial neural networks integrates the strength of both neural networks and traditional model-based methods for dynamical systems investigation. We provide a theoretical explanation of learning dynamical systems from time series for the proposed method, as well as demonstrate it in several applications. Namely, we show results of modeling and identification for both well-known systems like Lotka-Volterra equation and more complicated examples from retail, biochemistry, and accelerator physics.

Connected through hyperedge HE-1 (induced by source paper "polynomial neural networks and taylor maps for dynamical systems simulation and learning"):
  with members:
    - "review ordinary differential equations for deep learning"
    - "lie transform based neural networks for dynamics simulation and learning"
    - "neural ordinary differential equations"
    - "constructing runge kutta methods with the use of artificial 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.NE(Neural and Evolutionary Computing)