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: "assessing the performance of leja and clenshaw curtis collocation for computational electromagnetics with random input data"
  Abstract: We consider the problem of quantifying uncertainty regarding the output of an electromagnetic field problem in the presence of a large number of uncertain input parameters. In order to reduce the growth in complexity with the number of dimensions, we employ a dimension-adaptive stochastic collocation method based on nested univariate nodes. We examine the accuracy and performance of collocation schemes based on Clenshaw-Curtis and Leja rules, for the cases of uniform and bounded, non-uniform random inputs, respectively. Based on numerical experiments with an academic electromagnetic field model, we compare the two rules in both the univariate and multivariate case and for both quadrature and interpolation purposes. Results for a real-world electromagnetic field application featuring high-dimensional input uncertainty are also presented.

Connected through hyperedge HE-1 (induced by source paper "approximation and uncertainty quantification of systems with arbitrary parameter distributions using weighted leja interpolation"):
  with members:
    - "uncertainty quantification for nanoplasmonics with adjoint based leja adaptive collocation and conformal maps"
    - "adaptive smolyak pseudospectral approximations"

Connected through hyperedge HE-2 (induced by source paper "robust adaptive least squares polynomial chaos expansions in high frequency applications"):
  with members:
    - "uncertainty quantification for an optical grating coupler with an adjoint based leja adaptive collocation method"
    - "enhancing l 1 minimization estimates of polynomial chaos expansions using basis selection"

Connected through hyperedge HE-3 (induced by source paper "a multilevel monte carlo method for high dimensional uncertainty quantification of low frequency electromagnetic devices"):
  with members:
    - "modeling of spatial uncertainties in the magnetic reluctivity"
    - "multilevel monte carlo simulation of the eddy current problem with random parameters"

Connected through hyperedge HE-4 (induced by source paper "uncertainty modeling and analysis of the european x ray free electron laser cavities manufacturing process"):
  with members:
    - "approximation and uncertainty quantification of stochastic systems with arbitrary input distributions using weighted leja interpolation"
    - "uncertainty quantification for nanoplasmonics with adjoint based leja adaptive collocation and conformal maps"

Connected through hyperedge HE-5 (induced by source paper "adaptive newton monte carlo for efficient and fully error controlled yield optimization"):
  with members:
    - "uncertainty quantification for nanoplasmonics with adjoint based leja adaptive collocation and conformal maps"
    - "enhancing adaptive sparse grid approximations and improving refinement strategies using adjoint based a posteriori error estimates"

Connected through hyperedge HE-6 (induced by source paper "uncertainty quantification for nanoplasmonics with adjoint based leja adaptive collocation and conformal maps"):
  with members:
    - "approximation and uncertainty quantification of stochastic systems with arbitrary input distributions using weighted leja interpolation"
    - "enhancing adaptive sparse grid approximations and improving refinement strategies using adjoint based a posteriori error estimates"

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.CE(Computational Engineering, Finance, and Science)