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 "multi space variational encoder decoders for semi supervised labeled sequence transduction"):
  Abstract: Labeled sequence transduction is a task of transforming one sequence into another sequence that satisfies desiderata specified by a set of labels. In this paper we propose multi-space variational encoder-decoders, a new model for labeled sequence transduction with semi-supervised learning. The generative model can use neural networks to handle both discrete and continuous latent variables to exploit various features of data. Experiments show that our model provides not only a powerful supervised framework but also can effectively take advantage of the unlabeled data. On the SIGMORPHON morphological inflection benchmark, our model outperforms single-model state-of-art results by a large margin for the majority of languages.

Includes members:
  Member "controlling output length in neural encoder decoders":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generating representative headlines for news stories")
  Member "neural multi source morphological reinflection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural transductive learning and beyond morphological generation in the minimal resource setting")
  Member "single model encoder decoder with explicit morphological representation for reinflection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the conll sigmorphon 2018 shared task universal morphological reinflection")
  Member "variational neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mixpoet diverse poetry generation via learning controllable mixed latent space")
  Member "the concrete distribution a continuous relaxation of discrete random variables":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "domain decluttering simplifying images to mitigate synthetic real domain shift and improve depth estimation")
  Member "language as a latent variable discrete generative models for sentence compression":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a probabilistic formulation of unsupervised text style transfer")
  Member "semantic parsing with semi supervised sequential autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "exploring neural models for parsing natural language into first order logic")
  Member "google s multilingual neural machine translation system enabling zero shot translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "attention over parameters for dialogue systems")

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)