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: "approximation and uncertainty quantification of stochastic systems with arbitrary input distributions using weighted leja interpolation"
  Abstract: Approximation and uncertainty quantification methods based on Lagrange interpolation are typically abandoned in cases where the probability distributions of a stochastic system's input parameters are not normal, uniform, or closely related ones, due to the lack of suitable interpolation nodes. This paper suggests the use of weighted Leja node sequences as a remedy to this situation. We present the recently introduced weighted Leja interpolation rules, along with a dimension-adaptive sparse interpolation algorithm, to be employed in the case of high-dimensional input uncertainty. The performance and reliability of the suggested approach is verified by the results of three numerical experiments, where the respective models feature extreme value and truncated normal input distributions. The suggested approach is also compared against a well-established polynomial chaos method and found to be either comparable or superior in terms of approximation and statistical moment estimation accuracy.

Connected through hyperedge HE-1 (induced by source paper "uncertainty modeling and analysis of the european x ray free electron laser cavities manufacturing process"):
  with members:
    - "assessing the performance of leja and clenshaw curtis collocation for computational electromagnetics with random input data"
    - "uncertainty quantification for nanoplasmonics with adjoint based leja adaptive collocation and conformal maps"

Connected through hyperedge HE-2 (induced by source paper "uncertainty quantification for nanoplasmonics with adjoint based leja adaptive collocation and conformal maps"):
  with members:
    - "assessing the performance of leja and clenshaw curtis collocation for computational electromagnetics with random input data"
    - "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.NA(Numerical Analysis)