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 "compositional semantic parsing across graphbanks"):
  Abstract: Most semantic parsers that map sentences to graph-based meaning representations are hand-designed for specific graphbanks. We present a compositional neural semantic parser which achieves, for the first time, competitive accuracies across a diverse range of graphbanks. Incorporating BERT embeddings and multi-task learning improves the accuracy further, setting new states of the art on DM, PAS, PSD, AMR 2015 and EDS.

Includes members:
  Member "amr parsing as sequence to graph transduction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "amr similarity metrics from principles")
  Member "learning joint semantic parsers from disjoint data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "text generation with exemplar based adaptive decoding")
  Member "parser training with heterogeneous treebanks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a systematic comparison of methods for low resource dependency parsing on genuinely low resource languages")
  Member "multitask parsing across semantic representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "broad coverage semantic parsing as transduction")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "amr dependency parsing with a typed semantic algebra":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "amr similarity metrics from principles")
  Member "simpler but more accurate semantic dependency parsing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficient sentence embedding via semantic subspace analysis")

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)