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: "stochastic testing method for transistor level uncertainty quantification based on generalized polynomial chaos"
  Abstract: Uncertainties have become a major concern in integrated circuit design. In order to avoid the huge number of repeated simulations in conventional Monte Carlo flows, this paper presents an intrusive spectral simulator for statistical circuit analysis. Our simulator employs the recently developed generalized polynomial chaos expansion to perform uncertainty quantification of nonlinear transistor circuits with both Gaussian and non-Gaussian random parameters. We modify the nonintrusive stochastic collocation (SC) method and develop an intrusive variant called stochastic testing (ST) method. Compared with the popular intrusive stochastic Galerkin (SG) method, the coupled deterministic equations resulting from our proposed ST method can be solved in a decoupled manner at each time point. At the same time, ST requires fewer samples and allows more flexible time step size controls than directly using a nonintrusive SC solver. These two properties make ST more efficient than SG and than existing SC methods, and more suitable for time-domain circuit simulation. Simulation results of several digital, analog and RF circuits are reported. Since our algorithm is based on generic mathematical models, the proposed ST algorithm can be applied to many other engineering problems.

Connected through hyperedge HE-1 (induced by source paper "prediction of multi dimensional spatial variation data via bayesian tensor completion"):
  with members:
    - "uncertainty quantification of electronic and photonic ics with non gaussian correlated process variations"
    - "tensor computation a new framework for high dimensional problems in eda"
    - "enabling high dimensional hierarchical uncertainty quantification by anova and tensor train decomposition"
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"
    - "stochastic testing simulator for integrated circuits and mems hierarchical and sparse techniques"

Connected through hyperedge HE-2 (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"
    - "calculation of generalized polynomial chaos basis functions and gauss quadrature rules in hierarchical uncertainty quantification"
    - "sequential compressed sensing"

Connected through hyperedge HE-3 (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-4 (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"
    - "efficient uncertainty quantification for the periodic steady state of forced and autonomous circuits"

Connected through hyperedge HE-5 (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"
    - "efficient uncertainty quantification for the periodic steady state of forced and autonomous circuits"
    - "tensor decompositions for learning latent variable models"

Connected through hyperedge HE-6 (induced by source paper "probabilistic power flow computation via low rank and sparse tensor recovery"):
  with members:
    - "enabling high dimensional hierarchical uncertainty quantification by anova and tensor train decomposition"

Connected through hyperedge HE-7 (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"
    - "efficient uncertainty quantification for the periodic steady state of forced and autonomous circuits"

Connected through hyperedge HE-8 (induced by source paper "calculation of generalized polynomial chaos basis functions and gauss quadrature rules in hierarchical uncertainty quantification"):
  with members:
    - "efficient uncertainty quantification for the periodic steady state of forced and autonomous circuits"

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)