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 "an empirical evaluation of amr parsing for legal documents"):
  Abstract: Many approaches have been proposed to tackle the problem of Abstract Meaning Representation (AMR) parsing, helps solving various natural language processing issues recently. In our paper, we provide an overview of different methods in AMR parsing and their performances when analyzing legal documents. We conduct experiments of different AMR parsers on our annotated dataset extracted from the English version of Japanese Civil Code. Our results show the limitations as well as open a room for improvements of current parsing techniques when applying in this complicated domain.

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 "amr parsing as graph prediction with latent alignment":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "amr similarity metrics from principles")
  Member "convamr abstract meaning representation parsing for legal document":
    also connected through other hyperedges:
      (none)
  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 "neural amr sequence to sequence models for parsing and generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "modeling global and local node contexts for text generation from knowledge graphs")
  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")

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)