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 "unsupervised pidgin text generation by pivoting english data and self training"):
  Abstract: West African Pidgin English is a language that is significantly spoken in West Africa, consisting of at least 75 million speakers. Nevertheless, proper machine translation systems and relevant NLP datasets for pidgin English are virtually absent. In this work, we develop techniques targeted at bridging the gap between Pidgin English and English in the context of natural language generation. %As a proof of concept, we explore the proposed techniques in the area of data-to-text generation. By building upon the previously released monolingual Pidgin English text and parallel English data-to-text corpus, we hope to build a system that can automatically generate Pidgin English descriptions from structured data. We first train a data-to-English text generation system, before employing techniques in unsupervised neural machine translation and self-training to establish the Pidgin-to-English cross-lingual alignment. The human evaluation performed on the generated Pidgin texts shows that, though still far from being practically usable, the pivoting + self-training technique improves both Pidgin text fluency and relevance.

Includes members:
  Member "select and attend towards controllable content selection in text generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "personalized patent claim generation and measurement")
  Member "zero shot paraphrase generation with multilingual language models":
    also connected through other hyperedges:
      (none)
  Member "revisiting self training for neural sequence generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "capturing document context inside sentence level neural machine translation models with self training")
  Member "pidginunmt unsupervised neural machine translation from west african pidgin to english":
    also connected through other hyperedges:
      (none)
  Member "neobility at semeval 2017 task 1 an attention based sentence similarity model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation")
  Member "unsupervised machine translation using monolingual corpora only":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised question decomposition for question answering")
  Member "unsupervised neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multilingual denoising pre training for neural machine translation")
  Member "neural text generation from structured data with application to the biography domain":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "variational template machine for data to text generation")

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)