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: "model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders"
  Abstract: Nearly all model-reduction techniques project the governing equations onto a linear subspace of the original state space. Such subspaces are typically computed using methods such as balanced truncation, rational interpolation, the reduced-basis method, and (balanced) POD. Unfortunately, restricting the state to evolve in a linear subspace imposes a fundamental limitation to the accuracy of the resulting reduced-order model (ROM). In particular, linear-subspace ROMs can be expected to produce low-dimensional models with high accuracy only if the problem admits a fast decaying Kolmogorov $n$-width (e.g., diffusion-dominated problems). Unfortunately, many problems of interest exhibit a slowly decaying Kolmogorov $n$-width (e.g., advection-dominated problems). To address this, we propose a novel framework for projecting dynamical systems onto nonlinear manifolds using minimum-residual formulations at the time-continuous and time-discrete levels; the former leads to manifold Galerkin projection, while the latter leads to manifold least-squares Petrov--Galerkin (LSPG) projection. We perform analyses that provide insight into the relationship between these proposed approaches and classical linear-subspace reduced-order models; we also derive a posteriori discrete-time error bounds for the proposed approaches. In addition, we propose a computationally practical approach for computing the nonlinear manifold, which is based on convolutional autoencoders from deep learning. Finally, we demonstrate the ability of the method to significantly outperform even the optimal linear-subspace ROM on benchmark advection-dominated problems, thereby demonstrating the method's ability to overcome the intrinsic $n$-width limitations of linear subspaces.

Connected through hyperedge HE-1 (induced by source paper "pressio enabling projection based model reduction for large scale nonlinear dynamical systems"):
  with members:
    - "stability conditions for the explicit integration of projection based nonlinear reduced order and hyper reduced structural mechanics finite element models"
    - "conservative model reduction for finite volume models"
    - "space time least squares petrov galerkin projection for nonlinear model reduction"
    - "pymor generic algorithms and interfaces for model order reduction"
    - "preserving lagrangian structure in nonlinear model reduction with application to structural dynamics"
    - "occa a unified approach to multi threading languages"

Connected through hyperedge HE-2 (induced by source paper "data driven approximation of parametrized pdes by reduced basis and neural networks"):
  with members:
    - "a comprehensive analysis of deep regression"
    - "physics informed deep learning part ii data driven discovery of nonlinear partial differential equations"
    - "recent trends in deep learning based natural language processing"
    - "optimal approximation with sparsely connected deep neural networks"
    - "residual attention network for image classification"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-3 (induced by source paper "a discrete empirical interpolation method for interpretable immersion and embedding of nonlinear manifolds"):
  with members:
    - "too many secants a hierarchical approach to secant based dimensionality reduction on large data sets"
    - "a new selection operator for the discrete empirical interpolation method improved a priori error bound and extensions"

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.NA(Numerical Analysis)