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 robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
  Abstract: Recent work has managed to learn cross-lingual word embeddings without parallel data by mapping monolingual embeddings to a shared space through adversarial training. However, their evaluation has focused on favorable conditions, using comparable corpora or closely-related languages, and we show that they often fail in more realistic scenarios. This work proposes an alternative approach based on a fully unsupervised initialization that explicitly exploits the structural similarity of the embeddings, and a robust self-learning algorithm that iteratively improves this solution. Our method succeeds in all tested scenarios and obtains the best published results in standard datasets, even surpassing previous supervised systems. Our implementation is released as an open source project at this https URL

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"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-3 (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-4 (induced by source paper "improving candidate generation for low resource cross lingual entity linking"):
  with members:
    - "towards zero resource cross lingual entity linking"
    - "massively multilingual transfer for ner"
    - "choosing transfer languages for cross lingual learning"
    - "joint multilingual supervision for cross lingual entity linking"
    - "unsupervised multilingual word embeddings"
    - "bootstrapping transliteration with constrained discovery for low resource languages"
    - "zero shot neural transfer for cross lingual entity linking"
    - "one for all towards language independent named entity linking"

Connected through hyperedge HE-5 (induced by source paper "two level transformer and auxiliary coherence modeling for improved text segmentation"):
  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"
    - "self attention with relative position representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "text segmentation as a supervised learning task"
    - "attention is all you need"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "combining pretrained high resource embeddings and subword representations for low resource languages"):
  with members:
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "learning word vectors for 157 languages"
    - "opennmt open source toolkit for neural machine translation"
    - "enriching word vectors with subword information"
    - "learning to understand phrases by embedding the dictionary"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (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"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "unsupervised neural machine translation"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "effective cross lingual transfer of neural machine translation models without shared vocabularies"):
  with members:
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "improving unsupervised word by word translation with language model and denoising autoencoder"
    - "cross lingual language model pretraining"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "unsupervised statistical machine translation"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "trivial transfer learning for low resource neural machine translation"
    - "universal neural machine translation for extremely low resource 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)