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: "the romes method for statistical modeling of reduced order model error"
  Abstract: This work presents a technique for statistically modeling errors introduced by reduced-order models. The method employs Gaussian-process regression to construct a mapping from a small number of computationally inexpensive “error indicators” to a distribution over the true error. The variance of this distribution can be interpreted as the (epistemic) uncertainty introduced by the reduced-order model. To model normed errors, the method employs existing rigorous error bounds and residual norms as indicators; numerical experiments show that the method leads to a near-optimal expected effectivity in contrast to typical error bounds. To model errors in general outputs, the method uses dual-weighted residuals---which are amenable to uncertainty control---as indicators. Experiments illustrate that correcting the reduced-order-model output with this surrogate can improve prediction accuracy by an order of magnitude; this contrasts with existing “multifidelity correction” approaches, which often fail for reduced-or...

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"
    - "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"
    - "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 "statistical closure modeling for reduced order models of stationary systems by the romes method"):
  with members:
    - "machine learning error models for approximate solutions to parameterized systems of nonlinear equations"
    - "error modeling for surrogates of dynamical systems using machine learning"
    - "galerkin v least squares petrov galerkin projection in nonlinear model reduction"
    - "adaptive h refinement for reduced order models"

Connected through hyperedge HE-3 (induced by source paper "error modeling for surrogates of dynamical systems using machine learning"):
  with members:
    - "galerkin v least squares petrov galerkin projection in nonlinear model reduction"
    - "adaptive h refinement for reduced order models"

Connected through hyperedge HE-4 (induced by source paper "multivariate predictions of local reduced order model errors and dimensions"):
  with members:
    - "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)