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 parsing as graph prediction with latent alignment"):
  Abstract: Abstract meaning representations (AMRs) are broad-coverage sentence-level semantic representations. AMRs represent sentences as rooted labeled directed acyclic graphs. AMR parsing is challenging partly due to the lack of annotated alignments between nodes in the graphs and words in the corresponding sentences. We introduce a neural parser which treats alignments as latent variables within a joint probabilistic model of concepts, relations and alignments. As exact inference requires marginalizing over alignments and is infeasible, we use the variational auto-encoding framework and a continuous relaxation of the discrete alignments. We show that joint modeling is preferable to using a pipeline of align and parse. The parser achieves the best reported results on the standard benchmark (74.4% on LDC2016E25).

Includes members:
  Member "toward abstractive summarization using semantic representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "structsum incorporating latent and explicit sentence dependencies for single document summarization")
  Member "a simple and accurate syntax agnostic neural model for dependency based semantic role labeling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "semantic role labeling with iterative structure refinement")
  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 "text summarization using abstract meaning representation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "point less more abstractive summarization with pointer generator networks")
  Member "addressing the data sparsity issue in neural amr parsing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "amr parsing as sequence to graph transduction")
  Member "encoding sentences with graph convolutional networks for semantic role labeling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "benchmarking graph neural networks")
  Member "language to logical form with neural attention":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsupervised question decomposition for question answering")
  Member "learning executable semantic parsers for natural language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "don t parse generate a sequence to sequence architecture for task oriented semantic parsing")

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)