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: "characterizing departures from linearity in word translation"
  Abstract: We investigate the behavior of maps learned by machine translation methods. The maps translate words by projecting between word embedding spaces of different languages. We locally approximate these maps using linear maps, and find that they vary across the word embedding space. This demonstrates that the underlying maps are non-linear. Importantly, we show that the locally linear maps vary by an amount that is tightly correlated with the distance between the neighborhoods on which they are trained. Our results can be used to test non-linear methods, and to drive the design of more accurate maps for word translation.

Connected through hyperedge HE-1 (induced by source paper "absent cross lingual sentence representation mapping with bidirectional gans"):
  with members:
    - "reinforcement knowledge graph reasoning for explainable recommendation"
    - "improving multilingual sentence embedding using bi directional dual encoder with additive margin softmax"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "oogan disentangling gan with one hot sampling and orthogonal regularization"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning joint multilingual sentence representations with neural machine translation"
    - "triangle generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "analyzing the limitations of cross lingual word embedding mappings"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "non adversarial unsupervised word translation"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "learning crosslingual word embeddings without bilingual corpora"
    - "bilingual distributed word representations from document aligned comparable data"
    - "improving zero shot learning by mitigating the hubness problem"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "bilingual dictionary induction for bantu languages"):
  with members:
    - "massively multilingual word embeddings"
    - "improving zero shot learning by mitigating the hubness problem"
    - "an autoencoder approach to learning bilingual word representations"
    - "efficient estimation of word representations in vector space"
    - "exploiting similarities among languages for machine translation"
    - "natural language processing almost from scratch"
    - "from frequency to meaning vector space models of semantics"

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)