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: "fast and accurate tensor completion with tensor trains a system identification approach"
  Abstract: We propose a novel tensor completion approach by equating it to a system identification task. The key is to regard the coordinates and values of the known entries as inputs and outputs, respectively. By assuming a tensor train format initialized with low-rank tensor cores, the latter are iteratively identified via a simple alternating linear scheme to reduce residuals. Experiments verify the superiority of the proposed scheme in terms of both speed and accuracy, where a speedup of up to 23$\times$ is observed compared to state-of-the-art tensor completion methods at a similar accuracy.

Connected through hyperedge HE-1 (induced by source paper "a comprehensive survey for low rank regularization"):
  with members:
    - "bilinear factor matrix norm minimization for robust pca algorithms and applications"
    - "side information for face completion a robust pca approach"
    - "discriminative block diagonal representation learning for image recognition"
    - "missing slice recovery for tensors using a low rank model in embedded space"
    - "scalable dense non rigid structure from motion a grassmannian perspective"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "large scale low rank matrix learning with nonconvex regularizers"
    - "side information in robust principal component analysis algorithms and applications"

Connected through hyperedge HE-2 (induced by source paper "deep compression of sum product networks on tensor networks"):
  with members:
    - "analysis and design of convolutional networks via hierarchical tensor decompositions"
    - "expressive power of recurrent neural networks"
    - "learning graph structured sum product networks for probabilistic semantic maps"
    - "parallelized tensor train learning of polynomial classifiers"
    - "on the latent variable interpretation in sum product networks"
    - "tensor network alternating linear scheme for mimo volterra system identification"
    - "learning both weights and connections for efficient neural networks"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"

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)