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: "word emdeddings through hellinger pca"
  Abstract: Word embeddings resulting from neural language models have been shown to be successful for a large variety of NLP tasks. However, such architecture might be difficult to train and time-consuming. Instead, we propose to drastically simplify the word embeddings computation through a Hellinger PCA of the word co-occurence matrix. We compare those new word embeddings with some well-known embeddings on NER and movie review tasks and show that we can reach similar or even better performance. Although deep learning is not really necessary for generating good word embeddings, we show that it can provide an easy way to adapt embeddings to specific tasks.

Connected through hyperedge HE-1 (induced by source paper "holodetect few shot learning for error detection"):
  with members:
    - "autoaugment learning augmentation policies from data"
    - "a formal framework for probabilistic unclean databases"
    - "on calibration of modern neural networks"
    - "snorkel rapid training data creation with weak supervision"
    - "holoclean holistic data repairs with probabilistic inference"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "wide deep learning for recommender systems"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-2 (induced by source paper "sparse lifting of dense vectors unifying word and sentence representations"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "on the dimensionality of word embedding"
    - "leveraging term banks for answering complex questions a case for sparse vectors"
    - "convolutional sequence to sequence learning"
    - "spine sparse interpretable neural embeddings"
    - "sparse overcomplete word vector representations"
    - "learning word representations with hierarchical sparse coding"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-3 (induced by source paper "enhancing semantic word representations by embedding deeper word relationships"):
  with members:
    - "a comparative study of word embeddings for reading comprehension"
    - "an ensemble method to produce high quality word embeddings"
    - "text understanding from scratch"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "named entity recognition with bidirectional lstm cnns"
    - "semantically conditioned lstm based natural language generation for spoken dialogue systems"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "secret semantically enhanced classification of real world tasks"):
  with members:
    - "an overview of embedding models of entities and relationships for knowledge base completion"
    - "muse modularizing unsupervised sense embeddings"
    - "cnn rnn a unified framework for multi label image classification"
    - "open question answering with weakly supervised embedding models"
    - "deep visual semantic alignments for generating image descriptions"
    - "zero shot learning through cross modal transfer"
    - "efficient estimation of word representations in vector space"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-5 (induced by source paper "a new corpus for low resourced sindhi language with word embeddings"):
  with members:
    - "learning word vectors for 157 languages"
    - "enriching word vectors with subword information"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "graph based bilingual word embedding for statistical machine translation"):
  with members:
    - "syntax directed attention for neural machine translation"
    - "mimicking word embeddings using subword rnns"
    - "chunk based bi scale decoder for neural machine translation"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "a simple regularization based algorithm for learning cross domain word embeddings"
    - "six challenges for neural machine translation"
    - "bilingual learning of multi sense embeddings with discrete autoencoders"
    - "cross lingual models of word embeddings an empirical comparison"

Connected through hyperedge HE-7 (induced by source paper "semi supervised multi task word embeddings"):
  with members:
    - "analogies explained towards understanding word embeddings"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "think globally embed locally locally linear meta embedding of words"
    - "matrix factorization using window sampling and negative sampling for improved word representations"
    - "a joint model for word embedding and word morphology"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-8 (induced by source paper "angular based word meta embedding learning"):
  with members:
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "matrix factorization using window sampling and negative sampling for improved word representations"
    - "enriching word vectors with subword information"
    - "a latent variable model approach to pmi based word embeddings"
    - "learning word meta embeddings by using ensembles of embedding sets"
    - "autoextend extending word embeddings to embeddings for synsets and lexemes"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "addressing the rare word problem in neural machine translation"

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)