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: "pointer based fusion of bilingual lexicons into neural machine translation"
  Abstract: Neural machine translation (NMT) systems require large amounts of high quality in-domain parallel corpora for training. State-of-the-art NMT systems still face challenges related to out-of-vocabulary words and dealing with low-resource language pairs. In this paper, we propose and compare several models for fusion of bilingual lexicons with an end-to-end trained sequence-to-sequence model for machine translation. The result is a fusion model with two information sources for the decoder: a neural conditional language model and a bilingual lexicon. This fusion model learns how to combine both sources of information in order to produce higher quality translation output. Our experiments show that our proposed models work well in relatively low-resource scenarios, and also effectively reduce the parameter size and training cost for NMT without sacrificing performance.

Connected through hyperedge HE-1 (induced by source paper "word2word a collection of bilingual lexicons for 3 564 language pairs"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "bilingual lexicon induction through unsupervised machine translation"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "neural machine translation for low resource languages using bilingual lexicon induced from comparable corpora"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "word translation without parallel data"
    - "a strong baseline for learning cross lingual word embeddings from sentence alignments"
    - "distributed representations of words and phrases and their compositionality"

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)