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"
  Abstract: Recurrent neural networks (RNNs), including long short-term memory (LSTM) RNNs, have produced state-of-the-art results on a variety of speech recognition tasks. However, these models are often too large in size for deployment on mobile devices with memory and latency constraints. In this work, we study mechanisms for learning compact RNNs and LSTMs via low-rank factorizations and parameter sharing schemes. Our goal is to investigate redundancies in recurrent architectures where compression can be admitted without losing performance. A hybrid strategy of using structured matrices in the bottom layers and shared low-rank factors on the top layers is found to be particularly effective, reducing the parameters of a standard LSTM by 75%, at a small cost of 0.3% increase in WER, on a 2,000-hr English Voice Search task.

Connected through hyperedge HE-1 (induced by source paper "soft threshold weight reparameterization for learnable sparsity"):
  with members:
    - "rigging the lottery making all tickets winners"
    - "sparse networks from scratch faster training without losing performance"
    - "discovering neural wirings"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "fast sparse convnets"
    - "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"
    - "butterfly transform an efficient fft based neural architecture design"
    - "green ai"

Connected through hyperedge HE-2 (induced by source paper "image captioning with sparse recurrent neural network"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "comic toward a compact image captioning model with attention"
    - "amc automl for model compression and acceleration on mobile devices"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "snip single shot network pruning based on connection sensitivity"
    - "grow and prune compact fast and accurate lstms"
    - "compressing neural networks using the variational information bottleneck"

Connected through hyperedge HE-3 (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-4 (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"
    - "compressing recurrent neural network with tensor train"

Connected through hyperedge HE-5 (induced by source paper "run time efficient rnn compression for inference on edge devices"):
  with members:
    - "compressing rnns for iot devices by 15 38x using kronecker products"
    - "measuring scheduling efficiency of rnns for nlp applications"
    - "efficient recurrent neural networks using structured matrices in fpgas"
    - "structured weight matrices based hardware accelerators in deep neural networks fpgas and asics"
    - "alternating multi bit quantization for recurrent neural networks"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "learning compressed transforms with low displacement rank"
    - "block sparse recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "restricted recurrent neural networks"):
  with members:
    - "compression of recurrent neural networks for efficient language modeling"
    - "performance of three slim variants of the long short term memory lstm layer"
    - "bayesian compression for natural language processing"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "lstm a search space odyssey"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "regularizing and optimizing lstm language models"
    - "compressing recurrent neural network with tensor train"

Connected through hyperedge HE-7 (induced by source paper "intrinsically sparse long short term memory networks"):
  with members:
    - "the loss landscape of overparameterized neural networks"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "snip single shot network pruning based on connection sensitivity"
    - "in datacenter performance analysis of a tensor processing unit"
    - "deep lstm for large vocabulary continuous speech recognition"
    - "learning intrinsic sparse structures within long short term memory"
    - "deep rewiring training very sparse deep networks"
    - "factorization tricks for lstm networks"

Connected through hyperedge HE-8 (induced by source paper "grow and prune compact fast and accurate lstms"):
  with members:
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "image captioning"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"
    - "exploring sparsity in recurrent neural networks"
    - "nest a neural network synthesis tool based on a grow and prune paradigm"
    - "qsgd communication efficient sgd via gradient quantization and encoding"
    - "fast compact and high quality lstm rnn based statistical parametric speech synthesizers for mobile devices"

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)