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: "bilingual distributed word representations from document aligned comparable data"
  Abstract: We propose a new model for learning bilingual word representations from non-parallel document-aligned data. Following the recent advances in word representation learning, our model learns dense real-valued word vectors, that is, bilingual word embeddings (BWEs). Unlike prior work on inducing BWEs which heavily relied on parallel sentence-aligned corpora and/or readily available translation resources such as dictionaries, the article reveals that BWEs may be learned solely on the basis of document-aligned comparable data without any additional lexical resources nor syntactic information. We present a comparison of our approach with previous state-of-the-art models for learning bilingual word representations from comparable data that rely on the framework of multilingual probabilistic topic modeling (MuPTM), as well as with distributional local context-counting models. We demonstrate the utility of the induced BWEs in two semantic tasks: (1) bilingual lexicon extraction, (2) suggesting word translations in context for polysemous words. Our simple yet effective BWE-based models significantly outperform the MuPTM-based and context-counting representation models from comparable data as well as prior BWE-based models, and acquire the best reported results on both tasks for all three tested language pairs.

Connected through hyperedge HE-1 (induced by source paper "cross lingual alignment vs joint training a comparative study and a simple unified framework"):
  with members:
    - "adversarial learning with contextual embeddings for zero resource cross lingual classification and ner"
    - "a survey of cross lingual word embedding models"
    - "bilingual lexicon induction through unsupervised machine translation"
    - "mass masked sequence to sequence pre training for language generation"
    - "density matching for bilingual word embedding"
    - "analyzing the limitations of cross lingual word embedding mappings"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "bilingual lexicon induction with semi supervision in non isometric embedding spaces"

Connected through hyperedge HE-2 (induced by source paper "leveraging pretrained word embeddings for part of speech tagging of code switching data"):
  with members:
    - "named entity recognition on code switched data overview of the calcs 2018 shared task"
    - "part of speech tagging for code switched data"
    - "creating a large multi layered representational repository of linguistic code switched arabic data"
    - "universal dependency parsing for hindi english code switching"
    - "joining hands exploiting monolingual treebanks for parsing of code mixing data"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "word translation without parallel data"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-3 (induced by source paper "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "improving unsupervised word by word translation with language model and denoising autoencoder"
    - "gromov wasserstein alignment of word embedding spaces"
    - "unsupervised multilingual word embeddings"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "phrase based neural unsupervised machine translation"
    - "a broad coverage challenge corpus for sentence understanding through inference"

Connected through hyperedge HE-4 (induced by source paper "code switching language modeling with bilingual word embeddings a case study for egyptian arabic english"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "graph based bilingual word embedding for statistical machine translation"
    - "theanolm an extensible toolkit for neural network language modeling"
    - "cross lingual models of word embeddings an empirical comparison"
    - "challenges of computational processing of code switching"
    - "do multi sense embeddings improve natural language understanding"
    - "multilingual models for compositional distributed semantics"

Connected through hyperedge HE-5 (induced by source paper "analyzing the limitations of cross lingual word embedding mappings"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "characterizing departures from linearity in word translation"
    - "non adversarial unsupervised word translation"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "learning crosslingual word embeddings without bilingual corpora"
    - "improving zero shot learning by mitigating the hubness problem"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "best practices for learning domain specific cross lingual embeddings"):
  with members:
    - "unsupervised machine translation using monolingual corpora only"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "massively multilingual word embeddings"
    - "bidirectional lstm crf models for sequence tagging"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "exploiting similarities among languages for machine translation"
    - "multilingual distributed representations without word alignment"

Connected through hyperedge HE-7 (induced by source paper "zero shot cross lingual classification using multilingual neural machine translation"):
  with members:
    - "deep contextualized word representations"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "learned in translation contextualized word vectors"
    - "stochastic answer networks for machine reading comprehension"
    - "semi supervised sequence tagging with bidirectional language models"
    - "baselines and test data for cross lingual inference"
    - "what makes imagenet good for transfer learning"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-8 (induced by source paper "improving cross lingual word embeddings by meeting in the middle"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "robust cross lingual hypernymy detection using dependency context"
    - "generalizing procrustes analysis for better bilingual dictionary induction"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "unsupervised machine translation using monolingual corpora only"
    - "semantic specialisation of distributional word vector spaces using monolingual and cross lingual constraints"
    - "word translation without parallel data"

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)