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: "unsupervised word mapping using structural similarities in monolingual embeddings"
  Abstract: Most existing methods of automatic bilingual dictionary induction rely on prior alignments between the source and target languages, such as parallel corpora or seed dictionaries. For many language pairs, such supervised alignments are not readily available. We propose an unsupervised approach for learning a bilingual dictionary for a pair of languages given their independently-learned monolingual word embeddings. The proposed method exploits local and global structures in monolingual vector spaces to align them such that similar words are mapped to each other. We show experimentally that the performance of the bilingual alignments learned using the unsupervised method is comparable to supervised bilingual alignments using a seed dictionary.

Connected through hyperedge HE-1 (induced by source paper "scalable cross lingual transfer of neural sentence embeddings"):
  with members:
    - "context aware cross lingual mapping"
    - "effective parallel corpus mining using bilingual sentence embeddings"
    - "a corpus for multilingual document classification in eight languages"
    - "evaluation of unsupervised compositional representations"
    - "evaluating compositionality in sentence embeddings"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "learning joint multilingual sentence representations with neural machine translation"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-2 (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"
    - "generalizing procrustes analysis for better bilingual dictionary induction"
    - "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 neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "context aware cross lingual mapping"):
  with members:
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "evaluation of unsupervised compositional representations"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "word translation without parallel data"
    - "cross lingual models of word embeddings an empirical comparison"
    - "enriching word vectors with subword information"
    - "one billion word benchmark for measuring progress in statistical language modeling"

Connected through hyperedge HE-4 (induced by source paper "unsupervised cross lingual transfer of word embedding spaces"):
  with members:
    - "style transfer from non parallel text by cross alignment"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "word translation without parallel data"
    - "unsupervised cross domain image generation"
    - "enriching word vectors with subword information"
    - "massively multilingual word embeddings"

Connected through hyperedge HE-5 (induced by source paper "learning unsupervised word mapping by maximizing mean discrepancy"):
  with members:
    - "an iterative closest point method for unsupervised word translation"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "unsupervised cross lingual transfer of word embedding spaces"
    - "unsupervised machine translation using monolingual corpora only"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "word translation without parallel data"
    - "enriching word vectors with subword information"

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)