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: "cross lingual induction and transfer of verb classes based on word vector space specialisation"
  Abstract: Existing approaches to automatic VerbNet-style verb classification are heavily dependent on feature engineering and therefore limited to languages with mature NLP pipelines. In this work, we propose a novel cross-lingual transfer method for inducing VerbNets for multiple languages. To the best of our knowledge, this is the first study which demonstrates how the architectures for learning word embeddings can be applied to this challenging syntactic-semantic task. Our method uses cross-lingual translation pairs to tie each of the six target languages into a bilingual vector space with English, jointly specialising the representations to encode the relational information from English VerbNet. A standard clustering algorithm is then run on top of the VerbNet-specialised representations, using vector dimensions as features for learning verb classes. Our results show that the proposed cross-lingual transfer approach sets new state-of-the-art verb classification performance across all six target languages explored in this work.

Connected through hyperedge HE-1 (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-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"
    - "generalizing procrustes analysis for better bilingual dictionary induction"

Connected through hyperedge HE-3 (induced by source paper "ruminating word representations with random noised masker"):
  with members:
    - "towards robust neural machine translation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "towards understanding regularization in batch normalization"
    - "attention is all you need"
    - "enriching word vectors with subword information"
    - "character level convolutional networks for text classification"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-4 (induced by source paper "post specialisation retrofitting vectors of words unseen in lexical resources"):
  with members:
    - "self normalizing neural networks"
    - "sigmoid weighted linear units for neural network function approximation in reinforcement learning"
    - "specialising word vectors for lexical entailment"
    - "word translation without parallel data"
    - "continuously learning neural dialogue management"
    - "neural belief tracker data driven dialogue state tracking"
    - "separated by an un common language towards judgment language informed vector space modeling"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "expansional retrofitting for word vector enrichment"):
  with members:
    - "deep contextualized word representations"
    - "extrofitting enriching word representation and its vector space with semantic lexicons"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "semantic specialisation of distributional word vector spaces using monolingual and cross lingual constraints"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "enriching word vectors with subword information"
    - "counter fitting word vectors to linguistic constraints"
    - "simverb 3500 a large scale evaluation set of verb similarity"

Connected through hyperedge HE-6 (induced by source paper "generalizing procrustes analysis for better bilingual dictionary induction"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "improving cross lingual word embeddings by meeting in the middle"
    - "word translation without parallel data"
    - "massively multilingual word embeddings"
    - "computing lexical contrast"
    - "exploiting similarities among languages for machine translation"

Connected through hyperedge HE-7 (induced by source paper "extrofitting enriching word representation and its vector space with semantic lexicons"):
  with members:
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "enriching word vectors with subword information"
    - "counter fitting word vectors to linguistic constraints"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "retrofitting word vectors to semantic lexicons"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "specialising word vectors for lexical entailment"):
  with members:
    - "robust cross lingual hypernymy detection using dependency context"
    - "a vector space for distributional semantics for entailment"
    - "hypernyms under siege linguistically motivated artillery for hypernymy detection"
    - "neural belief tracker data driven dialogue state tracking"
    - "improving hypernymy detection with an integrated path based and distributional method"
    - "a large annotated corpus for learning natural language inference"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "word representations via gaussian embedding"

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)