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 "core semantic first a top down approach for amr parsing"):
  Abstract: We introduce a novel scheme for parsing a piece of text into its Abstract Meaning Representation (AMR): Graph Spanning based Parsing (GSP). One novel characteristic of GSP is that it constructs a parse graph incrementally in a top-down fashion. Starting from the root, at each step, a new node and its connections to existing nodes will be jointly predicted. The output graph spans the nodes by the distance to the root, following the intuition of first grasping the main ideas then digging into more details. The \textit{core semantic first} principle emphasizes capturing the main ideas of a sentence, which is of great interest. We evaluate our model on the latest AMR sembank and achieve the state-of-the-art performance in the sense that no heuristic graph re-categorization is adopted. More importantly, the experiments show that our parser is especially good at obtaining the core semantics.

Includes members:
  Member "compositional semantic parsing across graphbanks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "broad coverage semantic parsing as transduction")
  Member "rewarding smatch transition based amr parsing with reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "amr parsing as sequence to graph transduction")
  Member "toward abstractive summarization using semantic representations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "structsum incorporating latent and explicit sentence dependencies for single document summarization")
  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 "an amr aligner tuned by transition based parser":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "amr similarity metrics from principles")
  Member "linguistically informed self attention for semantic role labeling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "augmenting visual question answering with semantic frame information in a multitask learning approach")
  Member "guided neural language generation for abstractive summarization using abstract meaning representation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "joint parsing and generation for abstractive summarization")

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)