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: "space time least squares petrov galerkin projection for nonlinear model reduction"
  Abstract: This work proposes a space-time least-squares Petrov-Galerkin (ST-LSPG) projection method for model reduction of nonlinear dynamical systems. In contrast to typical nonlinear model-reduction methods that first apply (Petrov-)Galerkin projection in the spatial dimension and subsequently apply time integration to numerically resolve the resulting low-dimensional dynamical system, the proposed method applies projection in space and time simultaneously. To accomplish this, the method first introduces a low-dimensional space-time trial subspace, which can be obtained by computing tensor decompositions of state-snapshot data. The method then computes discrete-optimal approximations in this space-time trial subspace by minimizing the residual arising after time discretization over all space and time in a weighted $\ell^2$-norm. This norm can be defined to enable complexity reduction (i.e., hyper-reduction) in time, which leads to space-time collocation and space-time GNAT variants of the ST-LSPG method. Advantages of the approach relative to typical spatial-projection-based nonlinear model reduction methods such as Galerkin projection and least-squares Petrov-Galerkin projection include: (1) a reduction of both the spatial and temporal dimensions of the dynamical system, (2) the removal of spurious temporal modes (e.g., unstable growth) from the state space, and (3) error bounds that exhibit slower growth in time. Numerical examples performed on model problems in fluid dynamics demonstrate the ability of the method to generate orders-of-magnitude computational savings relative to spatial-projection-based reduced-order models without sacrificing accuracy.

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"
    - "model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders"
    - "conservative model reduction for finite volume models"
    - "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 "conservative model reduction for finite volume models"):
  with members:
    - "galerkin v least squares petrov galerkin projection in nonlinear model reduction"
    - "preserving lagrangian structure in nonlinear model reduction with application to structural dynamics"
    - "adaptive h refinement for reduced order models"
    - "two step greedy algorithm for reduced order quadratures"

Connected through hyperedge HE-3 (induced by source paper "sns a solution based nonlinear subspace method for time dependent model order reduction"):
  with members:
    - "a new selection operator for the discrete empirical interpolation method improved a priori error bound and extensions"
    - "galerkin v least squares petrov galerkin projection in nonlinear model reduction"

Connected through hyperedge HE-4 (induced by source paper "data driven time parallelism via forecasting"):
  with members:
    - "galerkin v least squares petrov galerkin projection in nonlinear model reduction"
    - "explicit parallel in time integration of a linear acoustic advection system"

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)