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 "fill in the blanks imputing missing sentences for larger context neural machine translation"):
  Abstract: Most neural machine translation systems still translate sentences in isolation. To make further progress, a promising line of research additionally considers the surrounding context in order to provide the model potentially missing source-side information, as well as to maintain a coherent output. One difficulty in training such larger-context (i.e. document-level) machine translation systems is that context may be missing from many parallel examples. To circumvent this issue, two-stage approaches, in which sentence-level translations are post-edited in context, have recently been proposed. In this paper, we instead consider the viability of filling in the missing context. In particular, we consider three distinct approaches to generate the missing context: using random contexts, applying a copy heuristic or generating it with a language model. In particular, the copy heuristic significantly helps with lexical coherence, while using completely random contexts hurts performance on many long-distance linguistic phenomena. We also validate the usefulness of tagged back-translation. In addition to improving BLEU scores as expected, using back-translated data helps larger-context machine translation systems to better capture long-range phenomena.

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 "a survey on document level machine translation methods and evaluation")
  Member "tagged back translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tag less back translation")
  Member "lingvo a modular and scalable framework for sequence to sequence modeling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "microsoft translator at wmt 2019 towards large scale document level neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "capturing document context inside sentence level neural machine translation models with self training")
  Member "context aware monolingual repair for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "capturing document context inside sentence level neural machine translation models with self training")
  Member "when a good translation is wrong in context context aware machine translation improves on deixis ellipsis and lexical cohesion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "capturing document context inside sentence level neural machine translation models with self training")
  Member "a generalized framework of sequence generation with application to undirected sequence models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "parallel machine translation with disentangled context transformer")
  Member "achieving human parity on automatic chinese to english news translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gret global representation enhanced transformer")

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)