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 "uncertainty quantification for nanoplasmonics with adjoint based leja adaptive collocation and conformal maps"):
  Abstract: This work addresses uncertainties arising in the nano-scale fabrication of optical devices. The stochastic collocation method is used to propagate uncertainties in material and geometry to the scattering parameters of the system. A dimension-adaptive scheme based on weighted Leja nodes is employed to reduce the computational complexity. The underlying approximation spaces consist of either standard global polynomials or conformally mapped polynomials. Various numerical studies are reported, showing the benefits and drawbacks of using conformal maps for surrogate modeling. The adaptive algorithm is based on an adjoint-based error indicator, which can further be used for error correction of the (mapped) polynomial approximation. An optical grating coupler is used as a benchmark example from nano-plasmonics. For this model problem, the adaptive strategy allows us to conduct a thorough uncertainty analysis, taking into account a moderately large number of random parameters. Among various results, it is found that, interestingly, geometric sensitivities outweigh material-related sensitivities in the considered setting, even if the geometrical variations are rather small.

Includes members:
  Member "approximation and uncertainty quantification of stochastic systems with arbitrary input distributions using weighted leja interpolation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "uncertainty modeling and analysis of the european x ray free electron laser cavities manufacturing process")
  Member "assessing the performance of leja and clenshaw curtis collocation for computational electromagnetics with random input data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximation and uncertainty quantification of systems with arbitrary parameter distributions using weighted leja interpolation")
  Member "enhancing adaptive sparse grid approximations and improving refinement strategies using adjoint based a posteriori error estimates":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adaptive newton monte carlo for efficient and fully error controlled yield optimization")

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.CE(Computational Engineering, Finance, and Science)