human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "enabling high dimensional hierarchical uncertainty quantification by anova and tensor train decomposition"):
  Abstract: Hierarchical uncertainty quantification can reduce the computational cost of stochastic circuit simulation by employing spectral methods at different levels. This paper presents an efficient framework to simulate hierarchically some challenging stochastic circuits/systems that include high-dimensional subsystems. Due to the high parameter dimensionality, it is challenging to both extract surrogate models at the low level of the design hierarchy and to handle them in the high-level simulation. In this paper, we develop an efficient analysis of variance-based stochastic circuit/microelectromechanical systems simulator to efficiently extract the surrogate models at the low level. In order to avoid the curse of dimensionality, we employ tensor-train decomposition at the high level to construct the basis functions and Gauss quadrature points. As a demonstration, we verify our algorithm on a stochastic oscillator with four MEMS capacitors and 184 random parameters. This challenging example is efficiently simulated by our simulator at the cost of only 10min in MATLAB on a regular personal computer.

Includes members:
  Member "uncertainty quantification for integrated circuits stochastic spectral methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tensor computation a new framework for high dimensional problems in eda")
  Member "stochastic testing simulator for integrated circuits and mems hierarchical and sparse techniques":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "prediction of multi dimensional spatial variation data via bayesian tensor completion")
  Member "calculation of generalized polynomial chaos basis functions and gauss quadrature rules in hierarchical uncertainty quantification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "uncertainty quantification of electronic and photonic ics with non gaussian correlated process variations")
  Member "era of big data processing a new approach via tensor networks and tensor decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a parallel sparse tensor benchmark suite on cpus and gpus")
  Member "efficient uncertainty quantification for the periodic steady state of forced and autonomous circuits":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "tensor computation a new framework for high dimensional problems in eda")
  Member "stochastic testing method for transistor level uncertainty quantification based on generalized polynomial chaos":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "prediction of multi dimensional spatial variation data via bayesian tensor completion")
  Member "tensor decompositions for learning latent variable models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "convolutional tensor train lstm for spatio temporal learning")

Question: Please tell me which class the source paper 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)