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 strong baseline for learning cross lingual word embeddings from sentence alignments"
  Abstract: While cross-lingual word embeddings have been studied extensively in recent years, the qualitative differences between the different algorithms remain vague. We observe that whether or not an algorithm uses a particular feature set (sentence IDs) accounts for a significant performance gap among these algorithms. This feature set is also used by traditional alignment algorithms, such as IBM Model-1, which demonstrate similar performance to state-of-the-art embedding algorithms on a variety of benchmarks. Overall, we observe that different algorithmic approaches for utilizing the sentence ID feature space result in similar performance. This paper draws both empirical and theoretical parallels between the embedding and alignment literature, and suggests that adding additional sources of information, which go beyond the traditional signal of bilingual sentence-aligned corpora, may substantially improve cross-lingual word embeddings, and that future baselines should at least take such features into account.

Connected through hyperedge HE-1 (induced by source paper "semantic drift in multilingual representations"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "what do language representations really represent"
    - "blackbox meets blackbox representational similarity and stability analysis of neural language models and brains"
    - "filtering and mining parallel data in a joint multilingual space"
    - "xnli evaluating cross lingual sentence representations"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "multimodal grounding for language processing"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

Connected through hyperedge HE-2 (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-3 (induced by source paper "self knowledge distillation in natural language processing"):
  with members:
    - "attention is all you need"
    - "a neural knowledge language model"
    - "context dependent word representation for neural machine translation"
    - "yolo9000 better faster stronger"
    - "pointer sentinel mixture models"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "deep residual learning for image recognition"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-4 (induced by source paper "word2word a collection of bilingual lexicons for 3 564 language pairs"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "bilingual lexicon induction through unsupervised machine translation"
    - "pointer based fusion of bilingual lexicons into neural machine translation"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "neural machine translation for low resource languages using bilingual lexicon induced from comparable corpora"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "word translation without parallel data"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (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"

Connected through hyperedge HE-6 (induced by source paper "embedding learning through multilingual concept induction"):
  with members:
    - "graph based bilingual word embedding for statistical machine translation"
    - "emerging language spaces learned from massively multilingual corpora"
    - "past present future a computational investigation of the typology of tense in 1000 languages"
    - "learning language representations for typology prediction"
    - "cross lingual models of word embeddings an empirical comparison"
    - "continuous multilinguality with language vectors"
    - "massively multilingual word embeddings"
    - "multilingual language processing from bytes"

Connected through hyperedge HE-7 (induced by source paper "cross lingual word analogies using linear transformations between semantic spaces"):
  with members:
    - "evaluation of croatian word embeddings"
    - "matrix factorization using window sampling and negative sampling for improved word representations"
    - "cross lingual models of word embeddings an empirical comparison"
    - "enriching word vectors with subword information"
    - "massively multilingual word embeddings"
    - "new word analogy corpus for exploring embeddings of czech words"
    - "issues in evaluating semantic spaces using word analogies"
    - "unsupervised dialogue act induction using gaussian mixtures"

Connected through hyperedge HE-8 (induced by source paper "a stronger baseline for multilingual word embeddings"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "unsupervised multilingual word embeddings"
    - "embedding learning through multilingual concept induction"
    - "word translation without parallel data"
    - "cross lingual models of word embeddings an empirical comparison"
    - "massively multilingual word embeddings"
    - "leveraging monolingual data for crosslingual compositional word representations"
    - "multilingual models for compositional distributed semantics"

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)