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: "a new selection operator for the discrete empirical interpolation method improved a priori error bound and extensions"
  Abstract: This paper introduces a new framework for constructing the discrete empirical interpolation method (\sf DEIM) projection operator. The interpolation node selection procedure is formulated using the QR factorization with column pivoting, and it enjoys a sharper error bound for the \sf DEIM projection error. Furthermore, for a subspace $\mathcal{U}$ given as the range of an orthonormal ${\mathsf U}$, the \sf DEIM projection does not change if ${\mathsf U}$ is replaced by ${\mathsf U} \Omega$ with arbitrary unitary matrix $\Omega$. In a large-scale setting, the new approach allows modifications that use only randomly sampled rows of ${\mathsf U}$, but with the potential of producing good approximations with corresponding probabilistic error bounds. Another salient feature of the new framework is that robust and efficient software implementation is easily developed, based on readily available high performance linear algebra packages.

Connected through hyperedge HE-1 (induced by source paper "machine learning error models for approximate solutions to parameterized systems of nonlinear equations"):
  with members:
    - "error modeling for surrogates of dynamical systems using machine learning"
    - "multivariate predictions of local reduced order model errors and dimensions"
    - "galerkin v least squares petrov galerkin projection in nonlinear model reduction"
    - "the romes method for statistical modeling of reduced order model error"
    - "adaptive h refinement for reduced order models"
    - "api design for machine learning software experiences from the scikit learn project"

Connected through hyperedge HE-2 (induced by source paper "a discrete empirical interpolation method for interpretable immersion and embedding of nonlinear manifolds"):
  with members:
    - "model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders"
    - "too many secants a hierarchical approach to secant based dimensionality reduction on large data sets"

Connected through hyperedge HE-3 (induced by source paper "sns a solution based nonlinear subspace method for time dependent model order reduction"):
  with members:
    - "space time least squares petrov galerkin projection for nonlinear model reduction"
    - "galerkin v least squares petrov galerkin projection in nonlinear model reduction"

Connected through hyperedge HE-4 (induced by source paper "the reduced order hybrid monte carlo sampling smoother"):
  with members:
    - "a hybrid monte carlo sampling smoother for four dimensional data assimilation"
    - "pod deim reduced order strategies for efficient four dimensional variational data assimilation"
    - "comparison of pod reduced order strategies for the nonlinear 2d shallow water equations"

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)