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: "the power and arnoldi methods in an algebra of circulants"
  Abstract: Circulant matrices play a central role in a recently proposed formulation of three-way data computations. In this setting, a three-way table corresponds to a matrix where each "scalar" is a vector of parameters defining a circulant. This interpretation provides many generalizations of results from matrix or vector-space algebra. We derive the power and Arnoldi methods in this algebra. In the course of our derivation, we define inner products, norms, and other notions. These extensions are straightforward in an algebraic sense, but the implications are dramatically different from the standard matrix case. For example, a matrix of circulants has a polynomial number of eigenvalues in its dimension; although, these can all be represented by a carefully chosen canonical set of eigenvalues and vectors. These results and algorithms are closely related to standard decoupling techniques on block-circulant matrices using the fast Fourier transform.

Connected through hyperedge HE-1 (induced by source paper "generalized visual information analysis via tensorial algebra"):
  with members:
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "sparse coding on symmetric positive definite manifolds using bregman divergences"
    - "extrinsic methods for coding and dictionary learning on grassmann manifolds"
    - "novel methods for multilinear data completion and de noising based on tensor svd"

Connected through hyperedge HE-2 (induced by source paper "exact matrix completion based on low rank hankel structure in the fourier domain"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "blind demixing and deconvolution at near optimal rate"
    - "fast and provable algorithms for spectrally sparse signal reconstruction via low rank hankel matrix completion"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "incoherence optimal matrix completion"
    - "exact tensor completion using t svd"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"
    - "robust spectral compressed sensing via structured matrix completion"

Connected through hyperedge HE-3 (induced by source paper "exact recovery of tensor robust principal component analysis under linear transforms"):
  with members:
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "a unified alternating direction method of multipliers by majorization minimization"
    - "incoherence optimal matrix completion"
    - "exact tensor completion using t svd"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "a new convex relaxation for tensor completion"
    - "most tensor problems are np hard"
    - "robust principal component analysis"

Connected through hyperedge HE-4 (induced by source paper "sturm sparse tubal regularized multilinear regression for fmri"):
  with members:
    - "boosted sparse and low rank tensor regression"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "exact tensor completion using t svd"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "a new convex relaxation for tensor completion"
    - "sparse overlapping sets lasso for multitask learning and its application to fmri analysis"
    - "tensor decompositions a new concept in brain data analysis"
    - "robust principal component analysis"

Connected through hyperedge HE-5 (induced by source paper "a novel nonconvex approach to recover the low tubal rank tensor data when t svd meets pssv"):
  with members:
    - "weighted low rank tensor recovery for hyperspectral image restoration"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "partial sum minimization of singular values in robust pca algorithm and applications"
    - "parallel matrix factorization for low rank tensor completion"
    - "exact tensor completion using t svd"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "generalized singular value thresholding"
    - "an alternating direction algorithm for matrix completion with nonnegative factors"

Connected through hyperedge HE-6 (induced by source paper "low tubal rank tensor completion using alternating minimization"):
  with members:
    - "low rank tensor completion a riemannian manifold preconditioning approach"
    - "adaptive sampling of rf fingerprints for fine grained indoor localization"
    - "tensor ring decomposition"
    - "exact tensor completion using t svd"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "fast matrix completion without the condition number"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "the noisy power method a meta algorithm with applications"

Connected through hyperedge HE-7 (induced by source paper "exact tensor completion using t svd"):
  with members:
    - "adaptive sampling of rf fingerprints for fine grained indoor localization"
    - "incoherence optimal matrix completion"
    - "clustering multi way data a novel algebraic approach"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "tensor based formulation and nuclear norm regularization for multienergy computed tomography"
    - "a new convex relaxation for tensor completion"
    - "recovering low rank matrices from few coefficients in any basis"
    - "note on sampling without replacing from a finite collection of matrices"

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)