human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "practical semantic parsing for spoken language understanding"
  Abstract: Executable semantic parsing is the task of converting natural language utterances into logical forms that can be directly used as queries to get a response. We build a transfer learning framework for executable semantic parsing. We show that the framework is effective for Question Answering (QA we experiment with public data sets such as Overnight and NLmaps as well as with commercial SLU data. The experiments carried out on data sets that are different in nature show how executable semantic parsing can unify different areas of NLP such as Q&A and SLU.

Connected through hyperedge HE-1 (induced by source paper "introduction to neural network based approaches for question answering over knowledge graphs"):
  with members:
    - "pretrained transformers for simple question answering over knowledge graphs"
    - "learning to generalize from sparse and underspecified rewards"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "question generation from sql queries improves neural semantic parsing"
    - "towards a question answering system over the semantic web"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "policy shaping and generalized update equations for semantic parsing from denotations"
    - "sqlnet generating structured queries from natural language without reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "don t paraphrase detect rapid and effective data collection for semantic parsing"):
  with members:
    - "learning to learn semantic parsers from natural language supervision"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "end to end open domain question answering with"
    - "building a neural semantic parser from a domain ontology"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "improving text to sql evaluation methodology"
    - "building a conversational agent overnight with dialogue self play"
    - "allennlp a deep semantic natural language processing platform"

Connected through hyperedge HE-3 (induced by source paper "neural semantic parsing with anonymization for command understanding in general purpose service robots"):
  with members:
    - "improving semantic parsing for task oriented dialog"
    - "from commands to goal based dialogs a roadmap to achieve natural language interaction in robocup home"
    - "improving grounded natural language understanding through human robot dialog"
    - "improving text to sql evaluation methodology"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "coarse to fine decoding for neural semantic parsing"

Connected through hyperedge HE-4 (induced by source paper "semantically driven auto completion"):
  with members:
    - "gmail smart compose real time assisted writing"
    - "learning neural templates for text generation"
    - "a survey on semantic parsing"

Question: Please tell me which class the center node 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)