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 "a survey on document level machine translation methods and evaluation"):
  Abstract: Machine translation (MT) is an important task in natural language processing (NLP) as it automates the translation process and reduces the reliance on human translators. With the advent of neural networks, the translation quality surpasses that of the translations obtained using statistical techniques. Up until three years ago, all neural translation models translated sentences independently, without incorporating any extra-sentential information. The aim of this paper is to highlight the major works that have been undertaken in the space of document-level machine translation before and after the neural revolution so that researchers can recognise where we started from and which direction we are heading in. When talking about the literature in statistical machine translation (SMT), we focus on works which have tried to improve the translation of specific discourse phenomena, while in neural machine translation (NMT), we focus on works which use the wider context explicitly. In addition to this, we also cover the evaluation strategies that have been introduced to account for the improvements in this domain.

Includes members:
  Member "context in neural machine translation a review of models and evaluations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fill in the blanks imputing missing sentences for larger context neural machine translation")
  Member "findings of the third workshop on neural generation and translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "naver labs europe s systems for the document level generation and translation task at wngt 2019")
  Member "naver labs europe s systems for the document level generation and translation task at wngt 2019":
    also connected through other hyperedges:
      (none)
  Member "improving conditioning in context aware sequence to sequence models":
    also connected through other hyperedges:
      (none)
  Member "evaluating pronominal anaphora in machine translation an evaluation measure and a test suite":
    also connected through other hyperedges:
      (none)
  Member "using whole document context in neural machine translation":
    also connected through other hyperedges:
      (none)
  Member "english czech systems in wmt19 document level transformer":
    also connected through other hyperedges:
      (none)
  Member "when and why is document level context useful in neural machine translation":
    also connected through other hyperedges:
      (none)

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)