human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "neural machine translation for cebuano to tagalog with subword unit translation"):
  Abstract: The Philippines is an archipelago composed of 7, 641 different islands with more than 150 different languages. This linguistic differences and diversity, though may be seen as a beautiful feature, have contributed to the difficulty in the promotion of educational and cultural development of different domains in the country. An effective machine translation system solely dedicated to cater Philippine languages will surely help bridge this gap. In this research work, a never before applied approach for language translation to a Philippine language was used for a Cebuano to Tagalog translator. A Recurrent Neural Network was used to implement the translator using OpenNMT sequence modeling tool in TensorFlow. The performance of the translation was evaluated using the BLEU Score metric. For the Cebuano to Tagalog translation, BLEU produced a score of 20.01. A subword unit translation for verbs and copyable approach was performed where commonly seen mistranslated words from the source to the target were corrected. The BLEU score increased to 22.87. Though slightly higher, this score still indicates that the translation is somehow understandable but is not yet considered as a good translation.

Includes members:
  Member "neural machine translation of rare words with subword units":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "neural machine translation by jointly learning to align and translate":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "sequence to sequence learning with neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

Question: Please tell me which class the source paper 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)