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 "conditioned query generation for task oriented dialogue systems"):
  Abstract: Scarcity of training data for task-oriented dialogue systems is a well known problem that is usually tackled with costly and time-consuming manual data annotation. An alternative solution is to rely on automatic text generation which, although less accurate than human supervision, has the advantage of being cheap and fast. In this paper we propose a novel controlled data generation method that could be used as a training augmentation framework for closed-domain dialogue. Our contribution is twofold. First we show how to optimally train and control the generation of intent-specific sentences using a conditional variational autoencoder. Then we introduce a novel protocol called query transfer that allows to leverage a broad, unlabelled dataset to extract relevant information. Comparison with two different baselines shows that our method, in the appropriate regime, consistently improves the diversity of the generated queries without compromising their quality.

Includes members:
  Member "efficient semi supervised learning for natural language understanding by optimizing diversity":
    also connected through other hyperedges:
      (none)
  Member "data augmentation for spoken language understanding via joint variational generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "variational hierarchical dialog autoencoder for dialogue state tracking data augmentation")
  Member "spoken language understanding on the edge":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "small footprint open vocabulary keyword spotting with quantized lstm networks")
  Member "snips voice platform an embedded spoken language understanding system for private by design voice interfaces":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient intent detection with dual sentence encoders")
  Member "texygen a benchmarking platform for text generation models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to generate multiple style transfer outputs for an input sentence")
  Member "sequence to sequence data augmentation for dialogue language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "variational hierarchical dialog autoencoder for dialogue state tracking data augmentation")
  Member "supervised learning of universal sentence representations from natural language inference data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on contextual embeddings")
  Member "adversarially regularized autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial code learning for image generation")

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)