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: "a simple regularization based algorithm for learning cross domain word embeddings"
  Abstract: Learning word embeddings has received a significant amount of attention recently. Often, word embeddings are learned in an unsupervised manner from a large collection of text. The genre of the text typically plays an important role in the effectiveness of the resulting embeddings. How to effectively train word embedding models using data from different domains remains a problem that is underexplored. In this paper, we present a simple yet effective method for learning word embeddings based on text from different domains. We demonstrate the effectiveness of our approach through extensive experiments on various down-stream NLP tasks.

Connected through hyperedge HE-1 (induced by source paper "benchmark performance of machine and deep learning based methodologies for urdu text document classification"):
  with members:
    - "on correlation of features extracted by deep neural networks"
    - "a simple joint model for improved contextual neural lemmatization"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "word embeddings a survey"
    - "a robust hybrid approach for textual document classification"
    - "character n gram embeddings to improve rnn language models"
    - "large batch optimization for deep learning training bert in 76 minutes"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-2 (induced by source paper "semi supervised representation learning via dual autoencoders for domain adaptation"):
  with members:
    - "a survey on deep transfer learning"
    - "partial adversarial domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "domain adaptation for ear recognition using deep convolutional neural networks"
    - "an extended framework for marginalized domain adaptation"
    - "conditional adversarial domain adaptation"
    - "domain adaptation of recurrent neural networks for natural language understanding"
    - "return of frustratingly easy domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "text classification with word embedding regularization and soft similarity measure"):
  with members:
    - "fasttext zip compressing text classification models"
    - "compressing word embeddings via deep compositional code learning"
    - "compressing neural networks with the hashing trick"
    - "a comparative study on regularization strategies for embedding based neural networks"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "linspector multilingual probing tasks for word representations"):
  with members:
    - "linspector web a multilingual probing suite for word representations"
    - "a survey of cross lingual word embedding models"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "xnli evaluating cross lingual sentence representations"
    - "on the complexity and typology of inflectional morphological systems"
    - "allennlp a deep semantic natural language processing platform"
    - "unimorph 2 0 universal morphology"

Connected through hyperedge HE-5 (induced by source paper "fusing vector space models for domain specific applications"):
  with members:
    - "lifelong domain word embedding via meta learning"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "deep contextualized word representations"
    - "phrase based neural unsupervised machine translation"
    - "comparative study of cnn and rnn for natural language processing"
    - "inducing domain specific sentiment lexicons from unlabeled corpora"
    - "multichannel variable size convolution for sentence classification"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-6 (induced by source paper "learning word embeddings with domain awareness"):
  with members:
    - "parser training with heterogeneous treebanks"
    - "factors influencing the surprising instability of word embeddings"
    - "on the dimensionality of word embedding"
    - "labeling gaps between words recognizing overlapping mentions with mention separators"
    - "integrating distributional lexical contrast into word embeddings for antonym synonym distinction"
    - "bag of tricks for efficient text classification"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (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"
    - "six challenges for neural machine translation"
    - "bilingual learning of multi sense embeddings with discrete autoencoders"
    - "cross lingual models of word embeddings an empirical comparison"
    - "vocabulary manipulation for neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "revisiting skip gram negative sampling model with regularization"):
  with members:
    - "on the convergence of adam and beyond"
    - "understanding composition of word embeddings via tensor decomposition"
    - "riemannian optimization for skip gram negative sampling"
    - "advances in pre training distributed word representations"
    - "word2vec skip gram with negative sampling is a weighted logistic pca"
    - "greedy approaches to symmetric orthogonal tensor decomposition"
    - "neural architectures for named entity recognition"
    - "successive rank one approximations for nearly orthogonally decomposable symmetric tensors"

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)