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 "a pilot study for chinese sql semantic parsing"):
  Abstract: The task of semantic parsing is highly useful for dialogue and question answering systems. Many datasets have been proposed to map natural language text into SQL, among which the recent Spider dataset provides cross-domain samples with multiple tables and complex queries. We build a Spider dataset for Chinese, which is currently a low-resource language in this task area. Interesting research questions arise from the uniqueness of the language, which requires word segmentation, and also from the fact that SQL keywords and columns of DB tables are typically written in English. We compare character- and word-based encoders for a semantic parser, and different embedding schemes. Results show that word-based semantic parser is subject to segmentation errors and cross-lingual word embeddings are useful for text-to-SQL.

Includes members:
  Member "improving semantic parsing for task oriented dialog":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "don t parse generate a sequence to sequence architecture for task oriented semantic parsing")
  Member "towards complex text to sql in cross domain database with intermediate representation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "break it down a question understanding benchmark")
  Member "is word segmentation necessary for deep learning of chinese representations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "large scale pretraining for neural machine translation with tens of billions of sentence pairs")
  Member "grammar based neural text to sql generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bertrand dr improving text to sql using a discriminative re ranker")
  Member "representing schema structure with graph neural networks for text to sql parsing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "how far are we from effective context modeling an exploratory study on semantic parsing in context")
  Member "seq2sql generating structured queries from natural language using reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "scalable neural methods for reasoning with a symbolic knowledge base")
  Member "chinese ner using lattice lstm":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "self attention based bigru and capsule network for named entity recognition")
  Member "sqlnet generating structured queries from natural language without reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "schema2qa answering complex queries on the structured web with a neural model")

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)