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: "effective dimensionality reduction for word embeddings"
  Abstract: Word embeddings have become the basic building blocks for several natural language processing and information retrieval tasks. Recently, there has been an emphasis on further improving the pre-trained word vectors through post-processing algorithms. One such area of improvement is the dimensionality reduction of word embeddings. Reducing the size of word embeddings through dimensionality reduction can improve their utility in memory constrained devices. In this work, we devise an algorithm that effectively combines PCA based dimensionality reduction with a post-processing algorithm, to construct word embeddings of lower dimensions. Empirical evalutions on 12 standard word similarity benchmarks show that our algorithm reduces the embedding dimensionality by 50\%, while achieving similar or (more often) better performance.

Connected through hyperedge HE-1 (induced by source paper "normalization of input output shared embeddings in text generation models"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "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"
    - "levenshtein transformer"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "transformers without tears improving the normalization of self attention"

Connected through hyperedge HE-2 (induced by source paper "lucene for approximate nearest neighbors search on arbitrary dense vectors"):
  with members:
    - "efficient natural language response suggestion for smart reply"
    - "simple and effective dimensionality reduction for word embeddings"
    - "all but the top simple and effective postprocessing for word representations"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "online embedding compression for text classification using low rank matrix factorization"):
  with members:
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "fasttext zip compressing text classification models"
    - "compressing word embeddings via deep compositional code learning"
    - "sgdr stochastic gradient descent with restarts"
    - "learning structured sparsity in deep neural networks"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "enriching word vectors with subword information"
    - "learning compact recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "near lossless binarization of word embeddings"):
  with members:
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "fasttext zip compressing text classification models"
    - "learning k way d dimensional discrete code for compact embedding representations"
    - "compressing word embeddings via deep compositional code learning"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "enriching word vectors with subword information"
    - "simverb 3500 a large scale evaluation set of verb similarity"
    - "sparse overcomplete word vector representations"

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)