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: "learning bilingual word representations by marginalizing alignments"
  Abstract: We present a probabilistic model that simultaneously learns alignments and distributed representations for bilingual data. By marginalizing over word alignments the model captures a larger semantic context than prior work relying on hard alignments. The advantage of this approach is demonstrated in a cross-lingual classification task, where we outperform the prior published state of the art.

Connected through hyperedge HE-1 (induced by source paper "unsupervised cross lingual transfer of word embedding spaces"):
  with members:
    - "style transfer from non parallel text by cross alignment"
    - "unsupervised word mapping using structural similarities in monolingual embeddings"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "characterizing departures from linearity in word translation"):
  with members:
    - "word translation without parallel data"
    - "cross lingual models of word embeddings an empirical comparison"
    - "enriching word vectors with subword information"
    - "massively multilingual word embeddings"
    - "improving zero shot learning by mitigating the hubness problem"
    - "an autoencoder approach to learning bilingual word representations"
    - "distributed representations of words and phrases and their compositionality"
    - "exploiting similarities among languages for machine translation"

Connected through hyperedge HE-3 (induced by source paper "cluse cross lingual unsupervised sense embeddings"):
  with members:
    - "beyond bilingual multi sense word embeddings using multilingual context"
    - "word translation without parallel data"
    - "muse modularizing unsupervised sense embeddings"
    - "breaking sticks and ambiguities with adaptive skip gram"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "do multi sense embeddings improve natural language understanding"
    - "multilingual models for compositional distributed semantics"
    - "an autoencoder approach to learning bilingual word representations"

Connected through hyperedge HE-4 (induced by source paper "a cross architecture instruction embedding model for natural language processing inspired binary code analysis"):
  with members:
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "a stronger baseline for multilingual word embeddings"
    - "neural machine translation inspired binary code similarity comparison beyond function pairs"
    - "neural network based graph embedding for cross platform binary code similarity detection"
    - "deep learning for answer sentence selection"
    - "convolutional neural networks over tree structures for programming language processing"
    - "efficient estimation of word representations in vector space"
    - "exploiting similarities among languages for machine translation"

Connected through hyperedge HE-5 (induced by source paper "massively multilingual word embeddings"):
  with members:
    - "separated by an un common language towards judgment language informed vector space modeling"
    - "transition based dependency parsing with stack long short term memory"
    - "multilingual models for compositional distributed semantics"
    - "polyglot distributed word representations for multilingual nlp"
    - "intriguing properties of neural networks"
    - "efficient estimation of word representations in vector space"
    - "exploiting similarities among languages for machine translation"
    - "a universal part of speech tagset"

Connected through hyperedge HE-6 (induced by source paper "resolving out of vocabulary words with bilingual embeddings in machine translation"):
  with members:
    - "tailoring word embeddings for bilexical predictions an experimental comparison"
    - "efficient estimation of word representations in vector space"
    - "exploiting similarities among languages for machine translation"
    - "consistency of the group lasso and multiple kernel learning"

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)