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: "efficient uncertainty quantification for the periodic steady state of forced and autonomous circuits"
  Abstract: This brief proposes an uncertainty quantification method for the periodic steady-state (PSS) analysis with both Gaussian and non-Gaussian variations. Our stochastic testing formulation for the PSS problem provides superior efficiency over both Monte Carlo methods and existing spectral methods. The numerical implementation of a stochastic shooting Newton solver is presented for both forced and autonomous circuits. Simulation results on some analog/RF circuits are reported to show the effectiveness of our proposed algorithms.

Connected through hyperedge HE-1 (induced by source paper "tensor computation a new framework for high dimensional problems in eda"):
  with members:
    - "a big data approach to handle process variations uncertainty quantification by tensor recovery"
    - "a big data approach to handle many process variations tensor recovery and applications"
    - "tensorizing neural networks"
    - "bayesian sparse tucker models for dimension reduction and tensor completion"
    - "enabling high dimensional hierarchical uncertainty quantification by anova and tensor train decomposition"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"
    - "uncertainty quantification for integrated circuits stochastic spectral methods"

Connected through hyperedge HE-2 (induced by source paper "a big data approach to handle process variations uncertainty quantification by tensor recovery"):
  with members:
    - "probabilistic power flow computation via low rank and sparse tensor recovery"
    - "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"

Connected through hyperedge HE-3 (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"
    - "calculation of generalized polynomial chaos basis functions and gauss quadrature rules in hierarchical uncertainty quantification"
    - "era of big data processing a new approach via tensor networks and tensor decompositions"
    - "stochastic testing method for transistor level uncertainty quantification based on generalized polynomial chaos"
    - "tensor decompositions for learning latent variable models"

Connected through hyperedge HE-4 (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"
    - "calculation of generalized polynomial chaos basis functions and gauss quadrature rules in hierarchical uncertainty quantification"
    - "stochastic testing method for transistor level uncertainty quantification based on generalized polynomial chaos"

Connected through hyperedge HE-5 (induced by source paper "calculation of generalized polynomial chaos basis functions and gauss quadrature rules in hierarchical uncertainty quantification"):
  with members:
    - "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)