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: "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.

Connected through hyperedge HE-1 (induced by source paper "pasta a parallel sparse tensor algorithm benchmark suite"):
  with members:
    - "load balanced sparse mttkrp on gpus"
    - "tensor train decomposition on tensorflow t3f"
    - "a practical randomized cp tensor decomposition"
    - "software for sparse tensor decomposition on emerging computing architectures"
    - "spartan scalable parafac2 for large sparse data"
    - "tensor regression meets gaussian processes"
    - "a unified optimization approach for sparse tensor operations on gpus"
    - "learning from multiway data simple and efficient tensor regression"

Connected through hyperedge HE-2 (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"
    - "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"
    - "stochastic testing method for transistor level uncertainty quantification based on generalized polynomial chaos"

Connected through hyperedge HE-3 (induced by source paper "fast and accurate tensor completion with tensor trains a system identification approach"):
  with members:
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "efficient tensor completion low rank tensor train"
    - "tensor network alternating linear scheme for mimo volterra system identification"
    - "parallel matrix factorization for low rank tensor completion"
    - "incoherence optimal matrix completion"
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"
    - "novel methods for multilinear data completion and de noising based on tensor svd"

Connected through hyperedge HE-4 (induced by source paper "uncertainty quantification of electronic and photonic ics with non gaussian correlated process variations"):
  with members:
    - "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"
    - "stochastic testing method for transistor level uncertainty quantification based on generalized polynomial chaos"
    - "sequential compressed sensing"

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.NA(Numerical Analysis)