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 "when and why is document level context useful in neural machine translation"):
  Abstract: Document-level context has received lots of attention for compensating neural machine translation (NMT) of isolated sentences. However, recent advances in document-level NMT focus on sophisticated integration of the context, explaining its improvement with only a few selected examples or targeted test sets. We extensively quantify the causes of improvements by a document-level model in general test sets, clarifying the limit of the usefulness of document-level context in NMT. We show that most of the improvements are not interpretable as utilizing the context. We also show that a minimal encoding is sufficient for the context modeling and very long context is not helpful for NMT.

Includes members:
  Member "microsoft translator at wmt 2019 towards large scale document level neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "capturing document context inside sentence level neural machine translation models with self training")
  Member "selective attention for context aware neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "toward making the most of context in neural machine translation")
  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-3 (induced by source paper "capturing document context inside sentence level neural machine translation models with self training")
  Member "adaptive attention span in transformers":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "meta embeddings based on self attention")
  Member "has machine translation achieved human parity a case for document level evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on document level machine translation methods and evaluation")
  Member "improving the transformer translation model with document level context":
    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 "integrating weakly supervised word sense disambiguation into neural machine translation":
    also connected through other hyperedges:
      (none)
  Member "document level neural machine translation with hierarchical attention networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "incorporating bert into neural machine translation")

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)