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 network alternating linear scheme for mimo volterra system identification"
  Abstract: This article introduces two Tensor Network-based iterative algorithms for the identification of high-order discrete-time nonlinear multiple-input multiple-output (MIMO) Volterra systems. The system identification problem is rewritten in terms of a Volterra tensor, which is never explicitly constructed, thus avoiding the curse of dimensionality. It is shown how each iteration of the two identification algorithms involves solving a linear system of low computational complexity. The proposed algorithms are guaranteed to monotonically converge and numerical stability is ensured through the use of orthogonal matrix factorizations. The performance and accuracy of the two identification algorithms are illustrated by numerical experiments, where accurate degree-10 MIMO Volterra models are identified in about 1 second in Matlab on a standard desktop pc.

Connected through hyperedge HE-1 (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"
    - "tensor computation a new framework for high dimensional problems in eda"
    - "efficient tensor completion low rank tensor train"
    - "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-2 (induced by source paper "deep compression of sum product networks on tensor networks"):
  with members:
    - "fast and accurate tensor completion with tensor trains a system identification approach"
    - "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"
    - "learning both weights and connections for efficient neural networks"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"

Connected through hyperedge HE-3 (induced by source paper "matrix output extension of the tensor network kalman filter with an application in mimo volterra system identification"):
  with members:
    - "a tensor network kalman filter with an application in recursive mimo volterra system identification"

Connected through hyperedge HE-4 (induced by source paper "tensor network subspace identification of polynomial state space models"):
  with members:
    - "a tensor network kalman filter with an application in recursive mimo volterra system identification"

Connected through hyperedge HE-5 (induced by source paper "a tensor network kalman filter with an application in recursive mimo volterra system identification"):
  with members:
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "era of big data processing a new approach via tensor networks and tensor decompositions"

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)