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: "generalizing procrustes analysis for better bilingual dictionary induction"
  Abstract: Most recent approaches to bilingual dictionary induction find a linear alignment between the word vector spaces of two languages. We show that projecting the two languages onto a third, latent space, rather than directly onto each other, while equivalent in terms of expressivity, makes it easier to learn approximate alignments. Our modified approach also allows for supporting languages to be included in the alignment process, to obtain an even better performance in low resource settings.

Connected through hyperedge HE-1 (induced by source paper "on the robustness of unsupervised and semi supervised cross lingual word embedding learning"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "learning cross lingual word embeddings from twitter via distant supervision"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "why is unsupervised alignment of english embeddings from different algorithms so hard"
    - "the limitations of cross language word embeddings evaluation"
    - "xnli evaluating cross lingual sentence representations"
    - "unsupervised multilingual word embeddings"
    - "on difficulties of cross lingual transfer with order differences a case study on dependency parsing"

Connected through hyperedge HE-2 (induced by source paper "do we really need fully unsupervised cross lingual embeddings"):
  with members:
    - "on the robustness of unsupervised and semi supervised cross lingual word embedding learning"
    - "a survey of cross lingual word embedding models"
    - "analyzing the limitations of cross lingual word embedding mappings"
    - "improving unsupervised word by word translation with language model and denoising autoencoder"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "gromov wasserstein alignment of word embedding spaces"
    - "unsupervised multilingual word embeddings"
    - "a resource light method for cross lingual semantic textual similarity"

Connected through hyperedge HE-3 (induced by source paper "duality regularization for unsupervised bilingual lexicon induction"):
  with members:
    - "gromov wasserstein alignment of word embedding spaces"
    - "unsupervised statistical machine translation"
    - "learning multilingual word embeddings in latent metric space a geometric approach"
    - "non adversarial unsupervised word translation"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "unsupervised neural machine translation with weight sharing"
    - "unsupervised word mapping using structural similarities in monolingual embeddings"
    - "unsupervised neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "meemi a simple method for post processing cross lingual word embeddings"):
  with members:
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "gromov wasserstein alignment of word embedding spaces"
    - "the limitations of cross language word embeddings evaluation"
    - "on difficulties of cross lingual transfer with order differences a case study on dependency parsing"
    - "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"
    - "senteval an evaluation toolkit for universal sentence representations"

Connected through hyperedge HE-5 (induced by source paper "towards conceptual generalization in the embedding space"):
  with members:
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "graph embedding techniques applications and performance a survey"
    - "phrase based neural unsupervised machine translation"
    - "attention is all you need"
    - "word translation without parallel data"
    - "node2vec scalable feature learning for networks"
    - "enriching word vectors with subword information"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-6 (induced by source paper "lost in evaluation misleading benchmarks for bilingual dictionary induction"):
  with members:
    - "don t forget the long tail a comprehensive analysis of morphological generalization in bilingual lexicon induction"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "an iterative closest point method for unsupervised word translation"
    - "learning multilingual word embeddings in latent metric space a geometric approach"
    - "unimorph 2 0 universal morphology"
    - "non adversarial unsupervised word translation"
    - "unsupervised alignment of embeddings with wasserstein procrustes"

Connected through hyperedge HE-7 (induced by source paper "deep learning based unsupervised concept unification in the embedding space"):
  with members:
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "graph embedding techniques applications and performance a survey"
    - "unsupervised machine translation using monolingual corpora only"
    - "node2vec scalable feature learning for networks"
    - "enriching word vectors with subword information"
    - "generative adversarial text to image synthesis"
    - "deepwalk online learning of social representations"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-8 (induced by source paper "bilingual lexicon induction with semi supervision in non isometric embedding spaces"):
  with members:
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "learning multilingual word embeddings in latent metric space a geometric approach"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "word translation without parallel data"
    - "enriching word vectors with subword information"
    - "distributed representations of words and phrases and their compositionality"

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)