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 "tensor computation a new framework for high dimensional problems in eda"):
  Abstract: Many critical electronic design automation (EDA) problems suffer from the curse of dimensionality, i.e., the very fast-scaling computational burden produced by large number of parameters and/or unknown variables. This phenomenon may be caused by multiple spatial or temporal factors (e.g., 3-D field solvers discretizations and multirate circuit simulation), nonlinearity of devices and circuits, large number of design or optimization parameters (e.g., full-chip routing/placement and circuit sizing), or extensive process variations (e.g., variability /reliability analysis and design for manufacturability). The computational challenges generated by such high-dimensional problems are generally hard to handle efficiently with traditional EDA core algorithms that are based on matrix and vector computation. This paper presents “tensor computation” as an alternative general framework for the development of efficient EDA algorithms and tools. A tensor is a high-dimensional generalization of a matrix and a vector, and is a natural choice for both storing and solving efficiently high-dimensional EDA problems. This paper gives a basic tutorial on tensors, demonstrates some recent examples of EDA applications (e.g., nonlinear circuit modeling and high-dimensional uncertainty quantification), and suggests further open EDA problems where the use of tensor computation could be of advantage.

Includes members:
  Member "a big data approach to handle process variations uncertainty quantification by tensor recovery":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "htdet a clustering method using information entropy for hardware trojan detection")
  Member "a big data approach to handle many process variations tensor recovery and applications":
    also connected through other hyperedges:
      (none)
  Member "tensorizing neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sparse weight activation training")
  Member "bayesian sparse tucker models for dimension reduction and tensor completion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "vectorial dimension reduction for tensors based on bayesian inference")
  Member "enabling high dimensional hierarchical uncertainty quantification by anova and tensor train decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pasta a parallel sparse tensor algorithm benchmark suite")
  Member "tensor decompositions for signal processing applications from two way to multiway component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "tensor decompositions in deep learning")
  Member "bayesian cp factorization of incomplete tensors with automatic rank determination":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "low rank tensor grid for image completion")
  Member "uncertainty quantification for integrated circuits stochastic spectral methods":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "enabling high dimensional hierarchical uncertainty quantification by anova and tensor train decomposition")

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)