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 word embeddings"
  Abstract: Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic. However, these vector space representations (created through large-scale text analysis) are typically stored verbatim, since their internal structure is opaque. Using word-analogy tests to monitor the level of detail stored in compressed re-representations of the same vector space, the trade-offs between the reduction in memory usage and expressiveness are investigated. A simple scheme is outlined that can reduce the memory footprint of a state-of-the-art embedding by a factor of 10, with only minimal impact on performance. Then, using the same `bit budget', a binary (approximate) factorisation of the same space is also explored, with the aim of creating an equivalent representation with better interpretability.

Connected through hyperedge HE-1 (induced by source paper "understanding the downstream instability of word embeddings"):
  with members:
    - "on the downstream performance of compressed word embeddings"
    - "the influence of down sampling strategies on svd word embedding stability"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "factors influencing the surprising instability of word embeddings"
    - "on the dimensionality of word embedding"
    - "attention is all you need"

Connected through hyperedge HE-2 (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"
    - "tensorized embedding layers for efficient model compression"
    - "hierarchy based image embeddings for semantic image retrieval"
    - "block based singular value decomposition approach to matrix factorization for recommender systems"

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"
    - "tensorized embedding layers for efficient model compression"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "word2ket space efficient word embeddings inspired by quantum entanglement"):
  with members:
    - "on the downstream performance of compressed word embeddings"
    - "low memory neural network training a technical report"
    - "generating long sequences with sparse transformers"
    - "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"
    - "improving sequence to sequence learning via optimal transport"
    - "roberta a robustly optimized bert pretraining approach"
    - "the state of sparsity in deep neural networks"
    - "texar a modularized versatile and extensible toolkit for text generation"

Connected through hyperedge HE-5 (induced by source paper "autoencoder based articulatory to acoustic mapping for ultrasound silent speech interfaces"):
  with members:
    - "deep convolutional autoencoder based lossy image compression"
    - "searching for activation functions"
    - "sigmoid weighted linear units for neural network function approximation in reinforcement learning"
    - "stylebank an explicit representation for neural image style transfer"
    - "lipreading with long short term memory"
    - "deep learning and the information bottleneck principle"
    - "scheduled denoising autoencoders"

Connected through hyperedge HE-6 (induced by source paper "rotations and interpretability of word embeddings the case of the russian language"):
  with members:
    - "riemannian optimization for skip gram negative sampling"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "all but the top simple and effective postprocessing for word representations"
    - "human and machine judgements for russian semantic relatedness"
    - "enriching word vectors with subword information"
    - "correlation based intrinsic evaluation of word vector representations"
    - "russe the first workshop on russian semantic similarity"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "effective dimensionality reduction for word embeddings"):
  with members:
    - "all but the top simple and effective postprocessing for word representations"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "retrofitting word vectors to semantic lexicons"
    - "a tutorial on principal component analysis"
    - "distributed representations of words and phrases and their compositionality"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-8 (induced by source paper "simple and effective dimensionality reduction for word embeddings"):
  with members:
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "distributed representations of words and phrases and their compositionality"

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)