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: "learning compact recurrent neural networks with block term tensor decomposition"
  Abstract: Recurrent Neural Networks (RNNs) are powerful sequence modeling tools. However, when dealing with high dimensional inputs, the training of RNNs becomes computational expensive due to the large number of model parameters. This hinders RNNs from solving many important computer vision tasks, such as Action Recognition in Videos and Image Captioning. To overcome this problem, we propose a compact and flexible structure, namely Block-Term tensor decomposition, which greatly reduces the parameters of RNNs and improves their training efficiency. Compared with alternative low-rank approximations, such as tensor-train RNN (TT-RNN), our method, Block-Term RNN (BT-RNN), is not only more concise (when using the same rank), but also able to attain a better approximation to the original RNNs with much fewer parameters. On three challenging tasks, including Action Recognition in Videos, Image Captioning and Image Generation, BT-RNN outperforms TT-RNN and the standard RNN in terms of both prediction accuracy and convergence rate. Specifically, BT-LSTM utilizes 17,388 times fewer parameters than the standard LSTM to achieve an accuracy improvement over 15.6\% in the Action Recognition task on the UCF11 dataset.

Connected through hyperedge HE-1 (induced by source paper "gate decorator global filter pruning method for accelerating deep convolutional neural networks"):
  with members:
    - "hetconv heterogeneous kernel based convolutions for deep cnns"
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-2 (induced by source paper "a tensorized transformer for language modeling"):
  with members:
    - "tensorized embedding layers for efficient model compression"
    - "transformer xl attentive language models beyond a fixed length context"
    - "universal transformers"
    - "deep contextualized word representations"
    - "an analysis of neural language modeling at multiple scales"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "west word encoded sequence transducers"
    - "groupreduce block wise low rank approximation for neural language model shrinking"

Connected through hyperedge HE-3 (induced by source paper "structured pruning of recurrent neural networks through neuron selection"):
  with members:
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "discrimination aware channel pruning for deep neural networks"
    - "compressing recurrent neural networks with tensor ring for action recognition"
    - "block sparse recurrent neural networks"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "learning intrinsic sparse structures within long short term memory"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-4 (induced by source paper "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"):
  with members:
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"
    - "kronecker recurrent units"
    - "on the state of the art of evaluation in neural language models"
    - "gated orthogonal recurrent units on learning to forget"
    - "regularizing and optimizing lstm language models"
    - "size independent sample complexity of neural networks"
    - "exploring sparsity in recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "one shot pruning of recurrent neural networks by jacobian spectrum evaluation"):
  with members:
    - "dynamical isometry and a mean field theory of lstms and grus"
    - "the state of sparsity in deep neural networks"
    - "trace norm regularization and faster inference for embedded speech recognition rnns"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "snip single shot network pruning based on connection sensitivity"
    - "grow and prune compact fast and accurate lstms"
    - "groupreduce block wise low rank approximation for neural language model shrinking"

Connected through hyperedge HE-6 (induced by source paper "few shot network compression via cross distillation"):
  with members:
    - "structured pruning of recurrent neural networks through neuron selection"
    - "rtn reparameterized ternary network"
    - "zero shot knowledge distillation in deep networks"
    - "scalable methods for 8 bit training of neural networks"
    - "proxquant quantized neural networks via proximal operators"
    - "few sample knowledge distillation for efficient network compression"
    - "knowledge distillation from few samples"
    - "rethinking the value of network pruning"

Connected through hyperedge HE-7 (induced by source paper "deep minimax probability machine"):
  with members:
    - "sparse dnns with improved adversarial robustness"
    - "compressing recurrent neural networks with tensor ring for action recognition"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "extending defensive distillation"
    - "end to end learning for self driving cars"
    - "universal adversarial perturbations"
    - "towards evaluating the robustness of neural networks"
    - "adversarial machine learning at scale"

Connected through hyperedge HE-8 (induced by source paper "compact autoregressive network"):
  with members:
    - "tensor decomposition for compressing recurrent neural network"
    - "compressing recurrent neural networks with tensor ring for action recognition"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "conditional image generation with pixelcnn decoders"
    - "wavenet a generative model for raw audio"
    - "tensorizing neural networks"
    - "compression of deep convolutional neural networks for fast and low power mobile applications"
    - "rethinking the inception architecture for computer vision"

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)