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: "parallelized tensor train learning of polynomial classifiers"
  Abstract: In pattern classification, polynomial classifiers are well-studied methods as they are capable of generating complex decision surfaces. Unfortunately, the use of multivariate polynomials is limited to kernels as in support vector machines, because polynomials quickly become impractical for high-dimensional problems. In this paper, we effectively overcome the curse of dimensionality by employing the tensor train format to represent a polynomial classifier. Based on the structure of tensor trains, two learning algorithms are proposed which involve solving different optimization problems of low computational complexity. Furthermore, we show how both regularization to prevent overfitting and parallelization, which enables the use of large training sets, are incorporated into these methods. Both the efficiency and efficacy of our tensor-based polynomial classifier are then demonstrated on the two popular datasets USPS and MNIST.

Connected through hyperedge HE-1 (induced by source paper "kernelized support tensor train machines"):
  with members:
    - "a support tensor train machine"
    - "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-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"
    - "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"

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"
    - "a support tensor train machine"
    - "tensor networks for dimensionality reduction and large scale optimization part 2 applications and future perspectives"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "a support tensor train machine"):
  with members:
    - "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)