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 "semantic graph parsing with recurrent neural network dag grammars"):
  Abstract: Semantic parses are directed acyclic graphs (DAGs), so semantic parsing should be modeled as graph prediction. But predicting graphs presents difficult technical challenges, so it is simpler and more common to predict the linearized graphs found in semantic parsing datasets using well-understood sequence models. The cost of this simplicity is that the predicted strings may not be well-formed graphs. We present recurrent neural network DAG grammars, a graph-aware sequence model that ensures only well-formed graphs while sidestepping many difficulties in graph prediction. We test our model on the Parallel Meaning Bank---a multilingual semantic graphbank. Our approach yields competitive results in English and establishes the first results for German, Italian and Dutch.

Includes members:
  Member "exploring neural methods for parsing discourse representation structures":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "don t parse generate a sequence to sequence architecture for task oriented semantic parsing")
  Member "learning deep generative models of graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hierarchical generation of molecular graphs using structural motifs")
  Member "sequence to action end to end semantic graph generation for semantic parsing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "semantic parsing with dual learning")
  Member "amr dependency parsing with a typed semantic algebra":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "amr parsing as sequence to graph transduction")
  Member "amr parsing as graph prediction with latent alignment":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "amr similarity metrics from principles")
  Member "towards universal semantic tagging":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "help a dataset for identifying shortcomings of neural models in monotonicity reasoning")
  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)