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: "embedding learning through multilingual concept induction"
  Abstract: We present a new method for estimating vector space representations of words: embedding learning by concept induction. We test this method on a highly parallel corpus and learn semantic representations of words in 1259 different languages in a single common space. An extensive experimental evaluation on crosslingual word similarity and sentiment analysis indicates that concept-based multilingual embedding learning performs better than previous approaches.

Connected through hyperedge HE-1 (induced by source paper "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "cross lingual language model pretraining"
    - "achieving human parity on automatic chinese to english news translation"
    - "effective parallel corpus mining using bilingual sentence embeddings"
    - "filtering and mining parallel data in a joint multilingual space"
    - "a corpus for multilingual document classification in eight languages"
    - "unsupervised statistical machine translation"
    - "xnli evaluating cross lingual sentence representations"

Connected through hyperedge HE-2 (induced by source paper "massively parallel cross lingual learning in low resource target language translation"):
  with members:
    - "what level of quality can neural machine translation attain on literary text"
    - "opennmt open source toolkit for neural machine translation"
    - "attention strategies for multi source sequence to sequence learning"
    - "improving lexical choice in neural machine translation"
    - "deep learning scaling is predictable empirically"
    - "toward multilingual neural machine translation with universal encoder and decoder"
    - "polyglot neural language models a case study in cross lingual phonetic representation learning"
    - "multi source neural translation"

Connected through hyperedge HE-3 (induced by source paper "a stronger baseline for multilingual word embeddings"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "unsupervised multilingual word embeddings"
    - "word translation without parallel data"
    - "cross lingual models of word embeddings an empirical comparison"
    - "a strong baseline for learning cross lingual word embeddings from sentence alignments"
    - "massively multilingual word embeddings"
    - "leveraging monolingual data for crosslingual compositional word representations"
    - "multilingual models for compositional distributed 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)