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: "on the limitations of unsupervised bilingual dictionary induction"
  Abstract: Unsupervised machine translation---i.e., not assuming any cross-lingual supervision signal, whether a dictionary, translations, or comparable corpora---seems impossible, but nevertheless, Lample et al. (2018) recently proposed a fully unsupervised machine translation (MT) model. The model relies heavily on an adversarial, unsupervised alignment of word embedding spaces for bilingual dictionary induction (Conneau et al., 2018), which we examine here. Our results identify the limitations of current unsupervised MT: unsupervised bilingual dictionary induction performs much worse on morphologically rich languages that are not dependent marking, when monolingual corpora from different domains or different embedding algorithms are used. We show that a simple trick, exploiting a weak supervision signal from identical words, enables more robust induction, and establish a near-perfect correlation between unsupervised bilingual dictionary induction performance and a previously unexplored graph similarity metric.

Connected through hyperedge HE-1 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity"):
  with members:
    - "multilingual alignment of contextual word representations"
    - "emerging cross lingual structure in pretrained language models"
    - "an empirical study on post processing methods for word embeddings"
    - "informing unsupervised pretraining with external linguistic knowledge"
    - "on the robustness of unsupervised and semi supervised cross lingual word embedding learning"
    - "word similarity datasets for thai construction and evaluation"
    - "paws paraphrase adversaries from word scrambling"
    - "cross lingual ability of multilingual bert an empirical study"

Connected through hyperedge HE-2 (induced by source paper "visual grounding in video for unsupervised word translation"):
  with members:
    - "end to end learning of visual representations from uncurated instructional videos"
    - "learning to follow directions in street view"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"
    - "unsupervised multi modal neural machine translation"
    - "learning visual question answering by bootstrapping hard attention"
    - "lifelong learning for image captioning by asking natural language questions"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-3 (induced by source paper "multilingual alignment of contextual word representations"):
  with members:
    - "cross lingual bert transformation for zero shot dependency parsing"
    - "a survey of cross lingual word embedding models"
    - "how multilingual is multilingual bert"
    - "context aware cross lingual mapping"
    - "simple and effective paraphrastic similarity from parallel translations"
    - "cross lingual language model pretraining"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "xnli evaluating cross lingual sentence representations"

Connected through hyperedge HE-4 (induced by source paper "lost in embedding space explaining cross lingual task performance with eigenvalue divergence"):
  with members:
    - "on the robustness of unsupervised and semi supervised cross lingual word embedding learning"
    - "a survey of cross lingual word embedding models"
    - "what do language representations really represent"
    - "analyzing the limitations of cross lingual word embedding mappings"
    - "bilingual lexicon induction with semi supervision in non isometric embedding spaces"
    - "investigating multilingual nmt representations at scale"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "do we really need fully unsupervised cross lingual embeddings"

Connected through hyperedge HE-5 (induced by source paper "refinement of unsupervised cross lingual word embeddings"):
  with members:
    - "a resource free evaluation metric for cross lingual word embeddings based on graph modularity"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "gromov wasserstein alignment of word embedding spaces"
    - "a discriminative latent variable model for bilingual lexicon induction"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "enriching word vectors with subword information"
    - "massively multilingual word embeddings"
    - "ridge regression hubness and zero shot learning"

Connected through hyperedge HE-6 (induced by source paper "unsupervised multilingual alignment using wasserstein barycenter"):
  with members:
    - "gromov wasserstein alignment of word embedding spaces"
    - "stochastic wasserstein barycenters"
    - "unsupervised multilingual word embeddings"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "unsupervised neural machine translation"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "improving pre trained multilingual models with vocabulary expansion"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "gromov wasserstein alignment of word embedding spaces"
    - "generating wikipedia by summarizing long sequences"
    - "bilingual expert can find translation errors"
    - "universal neural machine translation for extremely low resource languages"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-8 (induced by source paper "revisiting low resource neural machine translation a case study"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "revisiting character based neural machine translation with capacity and compression"
    - "unsupervised statistical machine translation"
    - "trivial transfer learning for low resource neural machine translation"
    - "pervasive attention 2d convolutional neural networks for sequence to sequence prediction"
    - "universal neural machine translation for extremely low resource languages"
    - "a call for clarity in reporting bleu scores"
    - "rapid adaptation of neural machine translation to new languages"

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)