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 "semantic parsing with dual learning"):
  Abstract: Semantic parsing converts natural language queries into structured logical forms. The paucity of annotated training samples is a fundamental challenge in this field. In this work, we develop a semantic parsing framework with the dual learning algorithm, which enables a semantic parser to make full use of data (labeled and even unlabeled) through a dual-learning game. This game between a primal model (semantic parsing) and a dual model (logical form to query) forces them to regularize each other, and can achieve feedback signals from some prior-knowledge. By utilizing the prior-knowledge of logical form structures, we propose a novel reward signal at the surface and semantic levels which tends to generate complete and reasonable logical forms. Experimental results show that our approach achieves new state-of-the-art performance on ATIS dataset and gets competitive performance on Overnight dataset.

Includes members:
  Member "agentgraph towards universal dialogue management with structured deep reinforcement learning":
    also connected through other hyperedges:
      (none)
  Member "a hierarchical decoding model for spoken language understanding from unaligned data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dialogue history integration into end to end signal to concept spoken language understanding systems")
  Member "question generation from sql queries improves neural semantic parsing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "introduction to neural network based approaches for question answering over knowledge graphs")
  Member "sequence to action end to end semantic graph generation for semantic parsing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "coupling retrieval and meta learning for context dependent semantic parsing")
  Member "structvae tree structured latent variable models for semi supervised semantic parsing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a probabilistic formulation of unsupervised text style transfer")
  Member "tranx a transition based neural abstract syntax parser for semantic parsing and code generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "structural language models of code")
  Member "coarse to fine decoding for neural semantic parsing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "structural language models of code")
  Member "dual supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "closed loop matters dual regression networks for single image super resolution")

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)