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 "chinese portuguese machine translation a study on building parallel corpora from comparable texts"):
  Abstract: Although there are increasing and significant ties between China and Portuguese-speaking countries, there is not much parallel corpora in the Chinese-Portuguese language pair. Both languages are very populous, with 1.2 billion native Chinese speakers and 279 million native Portuguese speakers, the language pair, however, could be considered as low-resource in terms of available parallel corpora. In this paper, we describe our methods to curate Chinese-Portuguese parallel corpora and evaluate their quality. We extracted bilingual data from Macao government websites and proposed a hierarchical strategy to build a large parallel corpus. Experiments are conducted on existing and our corpora using both Phrased-Based Machine Translation (PBMT) and the state-of-the-art Neural Machine Translation (NMT) models. The results of this work can be used as a benchmark for future Chinese-Portuguese MT systems. The approach we used in this paper also shows a good example on how to boost performance of MT systems for low-resource language pairs.

Includes members:
  Member "universal neural machine translation for extremely low resource languages":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "translating pro drop languages with reconstruction models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "go from the general to the particular multi domain translation with domain transformation networks")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "exploiting cross sentence context for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multilingual denoising pre training for neural machine translation")
  Member "modeling coverage for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "training simplification and model simplification for deep learning a minimal effort back propagation method")
  Member "neural machine translation of rare words with subword units":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (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-8 (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)