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: "calculation of generalized polynomial chaos basis functions and gauss quadrature rules in hierarchical uncertainty quantification"
  Abstract: Stochastic spectral methods are efficient techniques for uncertainty quantification. Recently they have shown excellent performance in the statistical analysis of integrated circuits. In stochastic spectral methods, one needs to determine a set of orthonormal polynomials and a proper numerical quadrature rule. The former are used as the basis functions in a generalized polynomial chaos expansion. The latter is used to compute the integrals involved in stochastic spectral methods. Obtaining such information requires knowing the density function of the random input a-priori. However, individual system components are often described by surrogate models rather than density functions. In order to apply stochastic spectral methods in hierarchical uncertainty quantification, we first propose to construct physically consistent closed-form density functions by two monotone interpolation schemes. Then, by exploiting the special forms of the obtained density functions, we determine the generalized polynomial-chaos basis functions and the Gauss quadrature rules that are required by a stochastic spectral simulator. The effectiveness of our proposed algorithm is verified by both synthetic and practical circuit examples.

Connected through hyperedge HE-1 (induced by source paper "uncertainty quantification of electronic and photonic ics with non gaussian correlated process variations"):
  with members:
    - "tensor computation a new framework for high dimensional problems in eda"
    - "enabling high dimensional hierarchical uncertainty quantification by anova and tensor train decomposition"
    - "stochastic testing simulator for integrated circuits and mems hierarchical and sparse techniques"
    - "stochastic testing method for transistor level uncertainty quantification based on generalized polynomial chaos"
    - "sequential compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "enabling high dimensional hierarchical uncertainty quantification by anova and tensor train decomposition"):
  with members:
    - "uncertainty quantification for integrated circuits stochastic spectral methods"
    - "stochastic testing simulator for integrated circuits and mems hierarchical and sparse techniques"
    - "era of big data processing a new approach via tensor networks and tensor decompositions"
    - "efficient uncertainty quantification for the periodic steady state of forced and autonomous circuits"
    - "stochastic testing method for transistor level uncertainty quantification based on generalized polynomial chaos"
    - "tensor decompositions for learning latent variable models"

Connected through hyperedge HE-3 (induced by source paper "stochastic testing simulator for integrated circuits and mems hierarchical and sparse techniques"):
  with members:
    - "uncertainty quantification for integrated circuits stochastic spectral methods"
    - "efficient uncertainty quantification for the periodic steady state of forced and autonomous circuits"
    - "stochastic testing method for transistor level uncertainty quantification based on generalized polynomial chaos"

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)