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 "paraphrase generation with latent bag of words"):
  Abstract: Paraphrase generation is a longstanding important problem in natural language processing. #R##N#In addition, recent progress in deep generative models has shown promising results on discrete latent variables for text generation. #R##N#Inspired by variational autoencoders with discrete latent structures, in this work, we propose a latent bag of words (BOW) model for paraphrase generation. #R##N#We ground the semantics of a discrete latent variable by the BOW from the target sentences. #R##N#We use this latent variable to build a fully differentiable content planning and surface realization model. #R##N#Specifically, we use source words to predict their neighbors and model the target BOW with a mixture of softmax. #R##N#We use Gumbel top-k reparameterization to perform differentiable subset sampling from the predicted BOW distribution. #R##N#We retrieve the sampled word embeddings and use them to augment the decoder and guide its generation search space. #R##N#Our latent BOW model not only enhances the decoder, but also exhibits clear interpretability. #R##N#We show the model interpretability with regard to \emph{(i)} unsupervised learning of word neighbors \emph{(ii)} the step-by-step generation procedure. #R##N#Extensive experiments demonstrate the transparent and effective generation process of this model.\footnote{Our code can be found at \url{this https URL}}

Includes members:
  Member "lagging inference networks and posterior collapse in variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial filters of dataset biases")
  Member "topic guided variational autoencoders for text generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "c textgen conditional text generation for harmonious human machine interaction")
  Member "step by step separating planning from realization in neural data to text generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "modeling global and local node contexts for text generation from knowledge graphs")
  Member "a tutorial on deep latent variable models of natural language":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "large scale pretraining for neural machine translation with tens of billions of sentence pairs")
  Member "end to end content and plan selection for data to text generation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stochastic natural language generation using dependency information")
  Member "learning neural templates for text generation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "torch struct deep structured prediction library")
  Member "no metrics are perfect adversarial reward learning for visual storytelling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "visual storytelling via predicting anchor word embeddings in the stories")

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)