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 "dual latent variable model for low resource natural language generation in dialogue systems"):
  Abstract: Recent deep learning models have shown improving results to natural language generation (NLG) irrespective of providing sufficient annotated data. However, a modest training data may harm such models performance. Thus, how to build a generator that can utilize as much of knowledge from a low-resource setting data is a crucial issue in NLG. This paper presents a variational neural-based generation model to tackle the NLG problem of having limited labeled dataset, in which we integrate a variational inference into an encoder-decoder generator and introduce a novel auxiliary autoencoding with an effective training procedure. Experiments showed that the proposed methods not only outperform the previous models when having sufficient training dataset but also show strong ability to work acceptably well when the training data is scarce.

Includes members:
  Member "a hybrid convolutional variational autoencoder for text generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deterministic decoding for discrete data in variational autoencoders")
  Member "natural language generation for spoken dialogue system using rnn encoder decoder networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "few shot natural language generation for task oriented dialog")
  Member "deconvolutional latent variable model for text sequence matching":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "topic guided variational autoencoders for text generation")
  Member "improved variational autoencoders for text modeling using dilated convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "blank language models")
  Member "deconvolutional paragraph representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "domain adaptive text style transfer")
  Member "multi domain neural network language generation for spoken dialogue systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "few shot natural language generation for task oriented dialog")
  Member "hierarchical multiscale recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "generating sentences from a continuous space":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "paraphrase generation with latent bag of words")

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)