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: "tensorized embedding layers for efficient model compression"
  Abstract: The embedding layers transforming input words into real vectors are the key components of deep neural networks used in natural language processing. However, when the vocabulary is large (e.g., 800k unique words in the One-Billion-Word dataset), the corresponding weight matrices can be enormous, which precludes their deployment in a limited resource setting. We introduce a novel way of parametrizing embedding layers based on the Tensor Train (TT) decomposition, which allows compressing the model significantly at the cost of a negligible drop or even a slight gain in performance. Importantly, our method does not take the pre-trained model and compress its weights but rather supplants the standard embedding layers with their TT-based counterparts. The resulting model is then trained end-to-end, however, it can capitalize on larger batches due to the reduced memory requirements. We evaluate our method on a wide range of benchmarks in sentiment analysis, neural machine translation, and language modeling, and analyze the trade-off between performance and compression ratios for a wide range of architectures, from MLPs to LSTMs and Transformers.

Connected through hyperedge HE-1 (induced by source paper "mixed dimension embeddings with application to memory efficient recommendation systems"):
  with members:
    - "on the dimensionality of embeddings for sparse features and data"
    - "deep learning recommendation model for personalization and recommendation systems"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "saec similarity aware embedding compression in recommendation systems"
    - "practice on long sequential user behavior modeling for click through rate prediction"
    - "hierarchy based image embeddings for semantic image retrieval"
    - "block based singular value decomposition approach to matrix factorization for recommender systems"
    - "on the difficulty of evaluating baselines a study on recommender systems"

Connected through hyperedge HE-2 (induced by source paper "a tensorized transformer for language modeling"):
  with members:
    - "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"
    - "adaptive input representations for neural language modeling"

Connected through hyperedge HE-3 (induced by source paper "on the downstream performance of compressed word embeddings"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "low memory neural network training a technical report"
    - "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"
    - "the state of sparsity in deep neural networks"
    - "high accuracy low precision training"
    - "low precision random fourier features for memory constrained kernel approximation"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"

Connected through hyperedge HE-4 (induced by source paper "distilled embedding non linear embedding factorization using knowledge distillation"):
  with members:
    - "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"
    - "groupreduce block wise low rank approximation for neural language model shrinking"
    - "online embedding compression for text classification using low rank matrix factorization"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "compositional embeddings using complementary partitions for memory efficient recommendation systems"):
  with members:
    - "on the dimensionality of embeddings for sparse features and data"
    - "deep learning recommendation model for personalization and recommendation systems"
    - "on the convergence of adam and beyond"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "mars memory attention aware recommender system"
    - "on the dimensionality of word embedding"
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "neural collaborative filtering"

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)