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: "groupreduce block wise low rank approximation for neural language model shrinking"
  Abstract: Model compression is essential for serving large deep neural nets on devices with limited resources or applications that require real-time responses. As a case study, a state-of-the-art neural language model usually consists of one or more recurrent layers sandwiched between an embedding layer used for representing input tokens and a softmax layer for generating output tokens. For problems with a very large vocabulary size, the embedding and the softmax matrices can account for more than half of the model size. For instance, the bigLSTM model achieves state-of- the-art performance on the One-Billion-Word (OBW) dataset with around 800k vocabulary, and its word embedding and softmax matrices use more than 6GBytes space, and are responsible for over 90% of the model parameters. In this paper, we propose GroupReduce, a novel compression method for neural language models, based on vocabulary-partition (block) based low-rank matrix approximation and the inherent frequency distribution of tokens (the power-law distribution of words). The experimental results show our method can significantly outperform traditional compression methods such as low-rank approximation and pruning. On the OBW dataset, our method achieved 6.6 times compression rate for the embedding and softmax matrices, and when combined with quantization, our method can achieve 26 times compression rate, which translates to a factor of 12.8 times compression for the entire model with very little degradation in perplexity.

Connected through hyperedge HE-1 (induced by source paper "learning multi granular quantized embeddings for large vocab categorical features in recommender systems"):
  with members:
    - "albert a lite bert for self supervised learning of language representations"
    - "differentiable product quantization for end to end embedding compression"
    - "self attentive sequential recommendation"
    - "adaptive input representations for neural language modeling"
    - "neural collaborative filtering"
    - "translation based recommendation"
    - "learning k way d dimensional discrete code for compact embedding representations"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"

Connected through hyperedge HE-2 (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-3 (induced by source paper "define deep factorized input word embeddings for neural sequence modeling"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "deep contextualized word representations"
    - "an analysis of neural language modeling at multiple scales"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "online embedding compression for text classification using low rank matrix factorization"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "adaptive input representations for neural language modeling"
    - "pyramidal recurrent unit for language modeling"

Connected through hyperedge HE-4 (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"
    - "block sparse recurrent neural networks"

Connected through hyperedge HE-5 (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"
    - "adaptive input representations for neural language modeling"

Connected through hyperedge HE-6 (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"
    - "block sparse recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "distilled embedding non linear embedding factorization using knowledge distillation"):
  with members:
    - "tensorized embedding layers for efficient model compression"
    - "multilingual neural machine translation with knowledge distillation"
    - "ctrl a conditional transformer language model for controllable generation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a call for clarity in reporting bleu scores"
    - "online embedding compression for text classification using low rank matrix factorization"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "learning low rank approximation for cnns"):
  with members:
    - "model compression via distillation and quantization"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "retraining based iterative weight quantization for deep neural networks"
    - "deeptwist learning model compression via occasional weight distortion"
    - "in datacenter performance analysis of a tensor processing unit"
    - "mec memory efficient convolution for deep neural network"
    - "tensorly tensor learning in python"
    - "learning both weights and connections for efficient neural networks"

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.CL(Computation and Language)