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 contraction layers for parsimonious deep nets"
  Abstract: Tensors offer a natural representation for many kinds of data frequently encountered in machine learning. Images, for example, are naturally represented as third order tensors, where the modes correspond to height, width, and channels. Tensor methods are noted for their ability to discover multi-dimensional dependencies, and tensor decompositions in particular, have been used to produce compact low-rank approximations of data. In this paper, we explore the use of tensor contractions as neural network layers and investigate several ways to apply them to activation tensors. Specifically, we propose the Tensor Contraction Layer (TCL), the first attempt to incorporate tensor contractions as end-to-end trainable neural network layers. Applied to existing networks, TCLs reduce the dimensionality of the activation tensors and thus the number of model parameters. We evaluate the TCL on the task of image recognition, augmenting two popular networks (AlexNet, VGG). The resulting models are trainable end-to-end. Applying the TCL to the task of image recognition, using the CIFAR100 and ImageNet datasets, we evaluate the effect of parameter reduction via tensor contraction on performance. We demonstrate significant model compression without significant impact on the accuracy and, in some cases, improved performance.

Connected through hyperedge HE-1 (induced by source paper "understanding generalization in deep learning via tensor methods"):
  with members:
    - "the singular values of convolutional layers"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "generalization error in deep learning"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"

Connected through hyperedge HE-2 (induced by source paper "t net parametrizing fully convolutional nets with a single high order tensor"):
  with members:
    - "quantized densely connected u nets for efficient landmark localization"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "cp decomposition with tensor power method for convolutional neural networks compression"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "tensorizing generative adversarial nets"
    - "binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources"
    - "mask r cnn"
    - "channel pruning for accelerating very deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "matrix and tensor decompositions for training binary neural networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "multi scale structure aware network for human pose estimation"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "cp decomposition with tensor power method for convolutional neural networks compression"
    - "wrpn training and inference using wide reduced precision networks"
    - "tensor regression networks"
    - "learning feature pyramids for human pose estimation"
    - "towards accurate binary convolutional neural network"

Connected through hyperedge HE-4 (induced by source paper "biologically inspired architectures for sample efficient deep reinforcement learning"):
  with members:
    - "random expert distillation imitation learning via expert policy support estimation"
    - "weight agnostic neural networks"
    - "scattering networks for hybrid representation learning"
    - "when to use parametric models in reinforcement learning"
    - "model based reinforcement learning for atari"
    - "kymatio scattering transforms in python"
    - "generative networks as inverse problems with scattering transforms"
    - "playing atari with six neurons"

Connected through hyperedge HE-5 (induced by source paper "robust deep networks with randomized tensor regression layers"):
  with members:
    - "randomized tensor ring decomposition and its application to large scale data reconstruction"
    - "regularization for deep learning a taxonomy"
    - "on the implicit bias of dropout"
    - "a practical randomized cp tensor decomposition"
    - "improving dnn robustness to adversarial attacks using jacobian regularization"
    - "tensor regression networks"
    - "tensor regression networks with various low rank tensor approximations"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"

Connected through hyperedge HE-6 (induced by source paper "efficient n dimensional convolutions via higher order factorization"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "cp decomposition with tensor power method for convolutional neural networks compression"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "aggregated residual transformations for deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "tensorly tensor learning in python"
    - "xception deep learning with depthwise separable convolutions"
    - "tensorizing neural networks"

Connected through hyperedge HE-7 (induced by source paper "compositional hierarchical tensor factorization representing hierarchical intrinsic and extrinsic causal factors"):
  with members:
    - "convolutional rectifier networks as generalized tensor decompositions"
    - "sparse hierarchical tucker factorization and its application to healthcare"
    - "an end to end system for unconstrained face verification with deep convolutional neural networks"
    - "tensorizing neural networks"
    - "compression of deep convolutional neural networks for fast and low power mobile applications"
    - "on the expressive power of deep learning a tensor analysis"
    - "theano new features and speed improvements"

Connected through hyperedge HE-8 (induced by source paper "improving efficiency in convolutional neural networks with multilinear filters"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "balanced quantization an effective and efficient approach to quantized neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "tensor representation in high frequency financial data for price change prediction"
    - "learning structured sparsity in deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "hardware oriented approximation of convolutional neural networks"
    - "quantized neural networks training neural networks with low precision weights and activations"

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)