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 "lattice based transformer encoder for neural machine translation"):
  Abstract: Neural machine translation (NMT) takes deterministic sequences for source representations. However, either word-level or subword-level segmentations have multiple choices to split a source sequence with different word segmentors or different subword vocabulary sizes. We hypothesize that the diversity in segmentations may affect the NMT performance. To integrate different segmentations with the state-of-the-art NMT model, Transformer, we propose lattice-based encoders to explore effective word or subword representation in an automatic way during training. We propose two methods: 1) lattice positional encoding and 2) lattice-aware self-attention. These two methods can be used together and show complementary to each other to further improve translation performance. Experiment results show superiorities of lattice-based encoders in word-level and subword-level representations over conventional Transformer encoder.

Includes members:
  Member "dependency or span end to end uniform semantic role labeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semantics aware bert for language understanding")
  Member "chinese word segmentation another decade review 2007 2017":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bert meets chinese word segmentation")
  Member "head driven phrase structure grammar parsing on penn treebank":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "parsing as pretraining")
  Member "revisiting character based neural machine translation with capacity and compression":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "revisiting low resource neural machine translation a case study")
  Member "nict s neural and statistical machine translation systems for the wmt18 news translation task":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the university of sydney s machine translation system for wmt19")
  Member "chinese ner using lattice lstm":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "self attention based bigru and capsule network for named entity recognition")
  Member "subword encoding in lattice lstm for chinese word segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "glyce glyph vectors for chinese character representations")
  Member "finding better subword segmentation for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "korean to chinese machine translation using chinese character as pivot clue")

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)