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: "a support tensor train machine"
  Abstract: There has been growing interest in extending traditional vector-based machine learning techniques to their tensor forms. An example is the support tensor machine (STM) that utilizes a rank-one tensor to capture the data structure, thereby alleviating the overfitting and curse of dimensionality problems in the conventional support vector machine (SVM). However, the expressive power of a rank-one tensor is restrictive for many real-world data. To overcome this limitation, we introduce a support tensor train machine (STTM) by replacing the rank-one tensor in an STM with a tensor train. Experiments validate and confirm the superiority of an STTM over the SVM and STM.

Connected through hyperedge HE-1 (induced by source paper "efficient structure preserving support tensor train machine"):
  with members:
    - "kernelized support tensor train machines"
    - "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"
    - "tensor decompositions a new concept in brain data analysis"
    - "learning tensors in reproducing kernel hilbert spaces with multilinear spectral penalties"

Connected through hyperedge HE-2 (induced by source paper "kernelized support tensor train machines"):
  with members:
    - "parallelized tensor train learning of polynomial classifiers"
    - "tensorizing neural networks"
    - "dusk a dual structure preserving kernel for supervised tensor learning with applications to neuroimages"
    - "speeding up convolutional neural networks using fine tuned cp decomposition"

Connected through hyperedge HE-3 (induced by source paper "the trouble with tensor ring decompositions"):
  with members:
    - "rank minimization on tensor ring a new paradigm in scalable tensor decomposition and completion"
    - "tensor networks for dimensionality reduction and large scale optimization part 2 applications and future perspectives"
    - "parallelized tensor train learning of polynomial classifiers"
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"
    - "tensor ring decomposition"
    - "tensorizing neural networks"
    - "speeding up convolutional neural networks using fine tuned cp decomposition"

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.LG(Machine Learning)