human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "machine learning error models for approximate solutions to parameterized systems of nonlinear equations"):
  Abstract: Abstract   This work proposes a machine-learning framework for constructing statistical models of errors incurred by approximate solutions to parameterized systems of nonlinear equations. These approximate solutions may arise from early termination of an iterative method, a lower-fidelity model, or a projection-based reduced-order model, for example. The proposed statistical model comprises the sum of a deterministic regression-function model and a stochastic noise model. The method constructs the regression-function model by applying regression techniques from machine learning (e.g., support vector regression, artificial neural networks) to map features (i.e., error indicators such as sampled elements of the residual) to a prediction of the approximate-solution error. The method constructs the noise model as a mean-zero Gaussian random variable whose variance is computed as the sample variance of the approximate-solution error on a test set; this variance can be interpreted as the epistemic uncertainty introduced by the approximate solution. This work considers a wide range of feature-engineering methods, data-set-construction techniques, and regression techniques that aim to ensure that (1) the features are cheaply computable, (2) the noise model exhibits low variance (i.e., low epistemic uncertainty introduced), and (3) the regression model generalizes to independent test data. Numerical experiments performed on several computational-mechanics problems and types of approximate solutions demonstrate the ability of the method to generate statistical models of the error that satisfy these criteria and significantly outperform more commonly adopted approaches for error modeling.

Includes members:
  Member "error modeling for surrogates of dynamical systems using machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "statistical closure modeling for reduced order models of stationary systems by the romes method")
  Member "multivariate predictions of local reduced order model errors and dimensions":
    also connected through other hyperedges:
      (none)
  Member "a new selection operator for the discrete empirical interpolation method improved a priori error bound and extensions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a discrete empirical interpolation method for interpretable immersion and embedding of nonlinear manifolds")
  Member "galerkin v least squares petrov galerkin projection in nonlinear model reduction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "statistical closure modeling for reduced order models of stationary systems by the romes method")
  Member "the romes method for statistical modeling of reduced order model error":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "statistical closure modeling for reduced order models of stationary systems by the romes method")
  Member "adaptive h refinement for reduced order models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "statistical closure modeling for reduced order models of stationary systems by the romes method")
  Member "api design for machine learning software experiences from the scikit learn project":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to segment 3d point clouds in 2d image space")

Question: Please tell me which class the source paper 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)