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 intrinsic sparse structures within long short term memory"
  Abstract: Model compression is significant for the wide adoption of Recurrent Neural Networks (RNNs) in both user devices possessing limited resources and business clusters requiring quick responses to large-scale service requests. This work aims to learn structurally-sparse Long Short-Term Memory (LSTM) by reducing the sizes of basic structures within LSTM units, including input updates, gates, hidden states, cell states and outputs. Independently reducing the sizes of basic structures can result in inconsistent dimensions among them, and consequently, end up with invalid LSTM units. To overcome the problem, we propose Intrinsic Sparse Structures (ISS) in LSTMs. Removing a component of ISS will simultaneously decrease the sizes of all basic structures by one and thereby always maintain the dimension consistency. By learning ISS within LSTM units, the obtained LSTMs remain regular while having much smaller basic structures. Based on group Lasso regularization, our method achieves 10.59x speedup without losing any perplexity of a language modeling of Penn TreeBank dataset. It is also successfully evaluated through a compact model with only 2.69M weights for machine Question Answering of SQuAD dataset. Our approach is successfully extended to non- LSTM RNNs, like Recurrent Highway Networks (RHNs). Our source code is publicly available at this https URL

Connected through hyperedge HE-1 (induced by source paper "fully quantized transformer for improved translation"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "efficient 8 bit quantization of transformer neural machine language translation model"
    - "weighted transformer network for machine translation"
    - "model compression via distillation and quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "scaling neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "groupreduce block wise low rank approximation for neural language model shrinking"

Connected through hyperedge HE-2 (induced by source paper "prunetrain fast neural network training by dynamic sparse model reconfiguration"):
  with members:
    - "mini batch serialization cnn training with inter layer data reuse"
    - "large scale language modeling converging on 40gb of text in four hours"
    - "amc automl for model compression and acceleration on mobile devices"
    - "imagenet training in minutes"
    - "focal loss for dense object detection"
    - "don t decay the learning rate increase the batch size"
    - "squeeze and excitation networks"
    - "mixed precision training"

Connected through hyperedge HE-3 (induced by source paper "deephoyer learning sparser neural network with differentiable scale invariant sparsity measures"):
  with members:
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "autopruner an end to end trainable filter pruning method for efficient deep model inference"
    - "amc automl for model compression and acceleration on mobile devices"
    - "leveraging filter correlations for deep model compression"
    - "discrimination aware channel pruning for deep neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "snip single shot network pruning based on connection sensitivity"
    - "soft filter pruning for accelerating deep convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "a one step pruning recovery framework for acceleration of convolutional neural networks"):
  with members:
    - "exploring linear relationship in feature map subspace for convnets compression"
    - "learning efficient convolutional networks through network slimming"
    - "nisp pruning networks using neuron importance score propagation"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "thinet a filter level pruning method for deep neural network compression"
    - "channel pruning for accelerating very deep neural networks"
    - "stacked hourglass networks for human pose estimation"
    - "learning structured sparsity in deep neural networks"

Connected through hyperedge HE-5 (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"
    - "regularizing and optimizing lstm language models"
    - "exploring sparsity in recurrent neural networks"

Connected through hyperedge HE-6 (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"
    - "tensor train recurrent neural networks for video classification"
    - "regularizing and optimizing lstm language models"
    - "factorization tricks for lstm networks"

Connected through hyperedge HE-7 (induced by source paper "compressing gradient optimizers via count sketches"):
  with members:
    - "large scale language modeling converging on 40gb of text in four hours"
    - "extreme classification in log memory"
    - "scaling neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "hash embeddings for efficient word representations"
    - "breaking the softmax bottleneck a high rank rnn language model"

Connected through hyperedge HE-8 (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"

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)