human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "efficient structure preserving support tensor train machine"):
  Abstract: Deploying the multi-relational tensor structure of a high dimensional feature space, more efficiently improves the performance of machine learning algorithms. One encounters the \emph{curse of dimensionality}, and working with vectorized data fails to preserve the data structure. To mitigate the nonlinear relationship of tensor data more economically, we propose the \emph{Tensor Train Multi-way Multi-level Kernel (TT-MMK)}. This technique combines kernel filtering of the initial input data (\emph{Kernelized Tensor Train (KTT)}), stable reparametrization of the KTT in the Canonical Polyadic (CP) format, and the Dual Structure-preserving Support Vector Machine (\emph{SVM}) Kernel for revealing nonlinear relationships. We demonstrate numerically that the TT-MMK method is more reliable computationally, is less sensitive to tuning parameters, and gives higher prediction accuracy in the SVM classification compared to similar tensorised SVM methods.

Includes members:
  Member "kernelized support tensor train machines":
    also connected through other hyperedges:
      (none)
  Member "a support tensor train machine":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "kernelized support tensor train machines")
  Member "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "convolutional tensor train lstm for spatio temporal learning")
  Member "dusk a dual structure preserving kernel for supervised tensor learning with applications to neuroimages":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "kernelized support tensor train machines")
  Member "speeding up convolutional neural networks using fine tuned cp decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "group sparsity the hinge between filter pruning and decomposition for network compression")
  Member "tensor decompositions a new concept in brain data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pasta a parallel sparse tensor algorithm benchmark suite")
  Member "learning tensors in reproducing kernel hilbert spaces with multilinear spectral penalties":
    also connected through other hyperedges:
      (none)

Question: Please tell me which class the source paper 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.LG(Machine Learning)