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 simulator for integrated circuits and mems hierarchical and sparse techniques"
  Abstract: Process variations are a major concern in today's chip design since they can significantly degrade chip performance. To predict such degradation, existing circuit and MEMS simulators rely on Monte Carlo algorithms, which are typically too slow. Therefore, novel fast stochastic simulators are highly desired. This paper first reviews our recently developed stochastic testing simulator that can achieve speedup factors of hundreds to thousands over Monte Carlo. Then, we develop a fast hierarchical stochastic spectral simulator to simulate a complex circuit or system consisting of several blocks. We further present a fast simulation approach based on anchored ANOVA (analysis of variance) for some design problems with many process variations. This approach can reduce the simulation cost and can identify which variation sources have strong impacts on the circuit's performance. The simulation results of some circuit and MEMS examples are reported to show the effectiveness of our simulator

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 method for transistor level uncertainty quantification based on generalized polynomial chaos"

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"
    - "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"
    - "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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "a big data approach to handle many process variations tensor recovery and applications"):
  with members:
    - "enabling high dimensional hierarchical uncertainty quantification by anova and tensor train decomposition"

Connected through hyperedge HE-6 (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"
    - "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"
    - "stochastic testing method for transistor level uncertainty quantification based on generalized polynomial chaos"
    - "tensor decompositions for learning latent variable models"

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)