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 "getting gender right in neural machine translation"):
  Abstract: Speakers of different languages must attend#R##N#to and encode strikingly different aspects of#R##N#the world in order to use their language correctly (Sapir, 1921; Slobin, 1996). One such#R##N#difference is related to the way gender is expressed in a language. Saying “I am happy”#R##N#in English, does not encode any additional#R##N#knowledge of the speaker that uttered the sentence. However, many other languages do#R##N#have grammatical gender systems and so such#R##N#knowledge would be encoded. In order to#R##N#correctly translate such a sentence into, say,#R##N#French, the inherent gender information needs#R##N#to be retained/recovered. The same sentence#R##N#would become either “Je suis heureux”, for a#R##N#male speaker or “Je suis heureuse” for a female one. Apart from morphological agreement, demographic factors (gender, age, etc.)#R##N#also influence our use of language in terms of#R##N#word choices or even on the level of syntactic constructions (Tannen, 1991; Pennebaker#R##N#et al., 2003). We integrate gender information#R##N#into NMT systems. Our contribution is twofold: (1) the compilation of large datasets with#R##N#speaker information for 20 language pairs, and#R##N#(2) a simple set of experiments that incorporate gender information into NMT for multiple language pairs. Our experiments show that#R##N#adding a gender feature to an NMT system significantly improves the translation quality for#R##N#some language pairs.

Includes members:
  Member "opennmt open source toolkit for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "prophetnet predicting future n gram for sequence to sequence pre training")
  Member "personalized machine translation preserving original author traits":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "text analysis in adversarial settings does deception leave a stylistic trace")
  Member "google s multilingual neural machine translation system enabling zero shot translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "attention over parameters for dialogue systems")
  Member "neural machine translation by jointly learning to align and translate":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "learning phrase representations using rnn encoder decoder for statistical machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "sequence to sequence learning with neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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)