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 "amr dependency parsing with a typed semantic algebra"):
  Abstract: We present a semantic parser for Abstract Meaning Representations which learns to parse strings into tree representations of the compositional structure of an AMR graph. This allows us to use standard neural techniques for supertagging and dependency tree parsing, constrained by a linguistically principled type system. We present two approximative decoding algorithms, which achieve state-of-the-art accuracy and outperform strong baselines.

Includes members:
  Member "amr parsing as graph prediction with latent alignment":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "amr similarity metrics from principles")
  Member "universal semantic parsing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reasoning driven question answering for natural language understanding")
  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 "a novel neural network model for joint pos tagging and graph based dependency parsing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an augmented transformer architecture for natural language generation tasks")
  Member "an incremental parser for abstract meaning representation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "amr similarity metrics from principles")
  Member "grammar as a foreign language":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "parsing as pretraining")

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)