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 "exploring neural methods for parsing discourse representation structures"):
  Abstract: Neural methods have had several recent successes in semantic parsing, though they have yet to face the challenge of producing meaning representations based on formal semantics. We present a sequence-to-sequence neural semantic parser that is able to produce Discourse Representation Structures (DRSs) for English sentences with high accuracy, outperforming traditional DRS parsers. To facilitate the learning of the output, we represent DRSs as a sequence of flat clauses and introduce a method to verify that produced DRSs are well-formed and interpretable. We compare models using characters and words as input and see (somewhat surprisingly) that the former performs better than the latter. We show that eliminating variable names from the output using De Bruijn-indices increases parser performance. Adding silver training data boosts performance even further.

Includes members:
  Member "evaluating scoped meaning representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey of cross lingual features for zero shot cross lingual semantic parsing")
  Member "opennmt open source toolkit for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "prophetnet predicting future n gram for sequence to sequence pre training")
  Member "neural semantic parsing by character based translation experiments with abstract meaning representations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "amr parsing as sequence to graph transduction")
  Member "stronger baselines for trustable results in neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi layer representation fusion for neural machine translation")
  Member "bpemb tokenization free pre trained subword embeddings in 275 languages":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generation distillation for efficient natural language understanding in low data settings")
  Member "massive exploration of neural machine translation architectures":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "meta embeddings based on self attention")
  Member "robust incremental neural semantic graph parsing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "exploring neural models for parsing natural language into first order logic")
  Member "the parallel meaning bank towards a multilingual corpus of translations annotated with compositional meaning representations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the linguistic representational power of neural machine translation models")

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)