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 decompositions a new concept in brain data analysis"
  Abstract: Matrix factorizations and their extensions to tensor facto rizations and decompositions have become prominent techniques for linear and multilinear blind source separation (BSS), especially multiway Independent Component Analysis (ICA), Nonnegative Matrix and Tensor Factorization (NMF/NTF), Smooth Component Analysis (SmoCA) and Sparse Component Analysis (SCA). Moreover, tensor decompositions have many other potential applications beyond multilinear BSS, especially feature extracti on, classification, dimensionality reduction and multiway clustering. In this paper, we briefly overview new and emerging models and approaches for tensor decompositions in applications to group and linked multiway BSS/ICA, feature extraction, classification and Multiway Parti al Least Squares (MPLS)

Connected through hyperedge HE-1 (induced by source paper "efficient structure preserving support tensor train machine"):
  with members:
    - "kernelized support tensor train machines"
    - "a support tensor train machine"
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"
    - "dusk a dual structure preserving kernel for supervised tensor learning with applications to neuroimages"
    - "speeding up convolutional neural networks using fine tuned cp decomposition"
    - "learning tensors in reproducing kernel hilbert spaces with multilinear spectral penalties"

Connected through hyperedge HE-2 (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"
    - "tensor computation a new framework for high dimensional problems in eda"
    - "a unified optimization approach for sparse tensor operations on gpus"

Connected through hyperedge HE-3 (induced by source paper "low rank tucker approximation of a tensor from streaming data"):
  with members:
    - "streaming low rank matrix approximation with an application to scientific simulation"
    - "tthresh tensor compression for multidimensional visual data"
    - "a practical randomized cp tensor decomposition"
    - "sketching for kronecker product regression and p splines"
    - "practical sketching algorithms for low rank matrix approximation"
    - "parallel tensor compression for large scale scientific data"
    - "decomposition of big tensors with low multilinear rank"
    - "low rank approximation and regression in input sparsity time"

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"
    - "the power and arnoldi methods in an algebra of circulants"
    - "robust principal component analysis"

Connected through hyperedge HE-5 (induced by source paper "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"):
  with members:
    - "randomized single view algorithms for low rank matrix approximation"
    - "group component analysis for multiblock data common and individual feature extraction"
    - "convolutional rectifier networks as generalized tensor decompositions"
    - "tensor networks for latent variable analysis part i algorithms for tensor train decomposition"
    - "linked component analysis from matrices to high order tensors applications to biomedical data"
    - "parallel tensor compression for large scale scientific data"
    - "tensor ring decomposition"
    - "smooth parafac decomposition for tensor completion"

Connected through hyperedge HE-6 (induced by source paper "sparse hierarchical tucker factorization and its application to healthcare"):
  with members:
    - "tensor networks for big data analytics and large scale optimization problems"
    - "era of big data processing a new approach via tensor networks and tensor decompositions"
    - "mach fast randomized tensor decompositions"
    - "relative error cur matrix 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)