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: "compressing recurrent neural network with tensor train"
  Abstract: Recurrent Neural Network (RNN) are a popular choice for modeling temporal and sequential tasks and achieve many state-of-the-art performance on various complex problems. However, most of the state-of-the-art RNNs have millions of parameters and require many computational resources for training and predicting new data. This paper proposes an alternative RNN model to reduce the number of parameters significantly by representing the weight parameters based on Tensor Train (TT) format. In this paper, we implement the TT-format representation for several RNN architectures such as simple RNN and Gated Recurrent Unit (GRU). We compare and evaluate our proposed RNN model with uncompressed RNN model on sequence classification and sequence prediction tasks. Our proposed RNNs with TT-format are able to preserve the performance while reducing the number of RNN parameters significantly up to 40 times smaller.

Connected through hyperedge HE-1 (induced by source paper "convolutional tensor train lstm for spatio temporal learning"):
  with members:
    - "a tensorized transformer for language modeling"
    - "efficient n dimensional convolutions via higher order factorization"
    - "robust deep networks with randomized tensor regression layers"
    - "scaling autoregressive video models"
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "stochastic adversarial video prediction"
    - "stochastic video generation with a learned prior"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-2 (induced by source paper "compressing language models using doped kronecker products"):
  with members:
    - "compression of recurrent neural networks for efficient language modeling"
    - "compressing rnns for iot devices by 15 38x using kronecker products"
    - "run time efficient rnn compression for inference on edge devices"
    - "pushing the limits of rnn compression"
    - "measuring scheduling efficiency of rnns for nlp applications"
    - "retraining based iterative weight quantization for deep neural networks"
    - "deeptwist learning model compression via occasional weight distortion"
    - "factorization tricks for lstm networks"

Connected through hyperedge HE-3 (induced by source paper "tensorized embedding layers for efficient model compression"):
  with members:
    - "a tensorized transformer for language modeling"
    - "pay less attention with lightweight and dynamic convolutions"
    - "transformer xl attentive language models beyond a fixed length context"
    - "albert a lite bert for self supervised learning of language representations"
    - "generalized tensor models for recurrent neural networks"
    - "word2bits quantized word vectors"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "west word encoded sequence transducers"

Connected through hyperedge HE-4 (induced by source paper "compressing rnns for iot devices by 15 38x using kronecker products"):
  with members:
    - "tucker tensor layer in fully connected neural networks"
    - "run time efficient rnn compression for inference on edge devices"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "measuring scheduling efficiency of rnns for nlp applications"
    - "the state of sparsity in deep neural networks"
    - "structured weight matrices based hardware accelerators in deep neural networks fpgas and asics"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"

Connected through hyperedge HE-5 (induced by source paper "on the effectiveness of low rank matrix factorization for lstm model compression"):
  with members:
    - "learning to prune filters in convolutional neural networks"
    - "deep contextualized word representations"
    - "end to end neural coreference resolution"
    - "on the state of the art of evaluation in neural language models"
    - "learned in translation contextualized word vectors"
    - "learning intrinsic sparse structures within long short term memory"
    - "tensor train recurrent neural networks for video classification"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-6 (induced by source paper "lossless compression for 3dcnns based on tensor train decomposition"):
  with members:
    - "tensor train decomposition on tensorflow t3f"
    - "tensor decomposition for compressing recurrent neural network"
    - "text independent speaker verification using 3d convolutional neural networks"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "expressive power of recurrent neural networks"
    - "deformable convolutional networks"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "tensor train recurrent neural networks for video classification"

Connected through hyperedge HE-7 (induced by source paper "compression of recurrent neural networks for efficient language modeling"):
  with members:
    - "trellis networks for sequence modeling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "bayesian compression for natural language processing"
    - "online embedding compression for text classification using low rank matrix factorization"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"
    - "pointing the unknown words"

Connected through hyperedge HE-8 (induced by source paper "a prior of a googol gaussians a tensor ring induced prior for generative models"):
  with members:
    - "gaussian mixture generative adversarial networks for diverse datasets and the unsupervised clustering of images"
    - "spectral normalization for generative adversarial networks"
    - "latent dirichlet allocation in generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "deep voice 3 scaling text to speech with convolutional sequence learning"
    - "progressive growing of gans for improved quality stability and variation"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "deep learning and quantum entanglement fundamental connections with implications to network design"

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)