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 "decoupling structure and lexicon for zero shot semantic parsing"):
  Abstract: Building a semantic parser quickly in a new domain is a fundamental challenge for conversational interfaces, as current semantic parsers require expensive supervision and lack the ability to generalize to new domains. In this paper, we introduce a zero-shot approach to semantic parsing that can parse utterances in unseen domains while only being trained on examples in other source domains. First, we map an utterance to an abstract, domain-independent, logical form that represents the structure of the logical form, but contains slots instead of KB constants. Then, we replace slots with KB constants via lexical alignment scores and global inference. Our model reaches an average accuracy of 53.4% on 7 domains in the Overnight dataset, substantially better than other zero-shot baselines, and performs as good as a parser trained on over 30% of the target domain examples.

Includes members:
  Member "polyglot semantic parsing in apis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "associating natural language comment and source code entities")
  Member "weakly supervised semantic parsing with abstract examples":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning semantic parsers from denotations with latent structured alignments and abstract programs")
  Member "towards zero shot frame semantic parsing for domain scaling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "zero resource cross domain named entity recognition")
  Member "neural semantic parsing over multiple knowledge bases":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semantic parsing with dual learning")
  Member "learning a neural semantic parser from user feedback":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "break it down a question understanding benchmark")
  Member "transfer learning for neural semantic parsing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spatialnli a spatial domain natural language interface to databases using spatial comprehension")
  Member "cross domain semantic parsing via paraphrasing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "paraphrase generation with latent bag of words")
  Member "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks")

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)