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 "uncertainty quantification of electronic and photonic ics with non gaussian correlated process variations"):
  Abstract: Since the invention of generalized polynomial chaos in 2002, uncertainty quantification has impacted many engineering fields, including variation-aware design automation of integrated circuits and integrated photonics. Due to the fast convergence rate, the generalized polynomial chaos expansion has achieved orders-of-magnitude speedup than Monte Carlo in many applications. However, almost all existing generalized polynomial chaos methods have a strong assumption: the uncertain parameters are mutually independent or Gaussian correlated. This assumption rarely holds in many realistic applications, and it has been a long-standing challenge for both theorists and practitioners. #R##N#This paper propose a rigorous and efficient solution to address the challenge of non-Gaussian correlation. We first extend generalized polynomial chaos, and propose a class of smooth basis functions to efficiently handle non-Gaussian correlations. Then, we consider high-dimensional parameters, and develop a scalable tensor method to compute the proposed basis functions. Finally, we develop a sparse solver with adaptive sample selections to solve high-dimensional uncertainty quantification problems. We validate our theory and algorithm by electronic and photonic ICs with 19 to 57 non-Gaussian correlated variation parameters. The results show that our approach outperforms Monte Carlo by $2500\times$ to $3000\times$ in terms of efficiency. Moreover, our method can accurately predict the output density functions with multiple peaks caused by non-Gaussian correlations, which is hard to handle by existing methods. #R##N#Based on the results in this paper, many novel uncertainty quantification algorithms can be developed and can be further applied to a broad range of engineering domains.

Includes members:
  Member "tensor computation a new framework for high dimensional problems in eda":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pasta a parallel sparse tensor algorithm benchmark suite")
  Member "enabling high dimensional hierarchical uncertainty quantification by anova and tensor train decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pasta a parallel sparse tensor algorithm benchmark suite")
  Member "stochastic testing simulator for integrated circuits and mems hierarchical and sparse techniques":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "enabling high dimensional hierarchical uncertainty quantification by anova and tensor train decomposition")
  Member "stochastic testing method for transistor level uncertainty quantification based on generalized polynomial chaos":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "prediction of multi dimensional spatial variation data via bayesian tensor completion")
  Member "sequential compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an active sequential xampling detector for spectrum sensing")

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.NA(Numerical Analysis)