human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "generative neural machine translation"
  Abstract: We introduce Generative Neural Machine Translation (GNMT), a latent variable architecture which is designed to model the semantics of the source and target sentences. We modify an encoder-decoder translation model by adding a latent variable as a language agnostic representation which is encouraged to learn the meaning of the sentence. GNMT achieves competitive BLEU scores on pure translation tasks, and is superior when there are missing words in the source sentence. We augment the model to facilitate multilingual translation and semi-supervised learning without adding parameters. This framework significantly reduces overfitting when there is limited paired data available, and is effective for translating between pairs of languages not seen during training.

Connected through hyperedge HE-1 (induced by source paper "towards generating long and coherent text with multi level latent variable models"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "topic guided variational autoencoders for text generation"
    - "variational sequential labelers for semi supervised learning"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "eval all trust a few do wrong to none comparing sentence generation models"
    - "learning to write with cooperative discriminators"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "nash toward end to end neural architecture for generative semantic hashing"

Connected through hyperedge HE-2 (induced by source paper "latent variable non autoregressive neural machine translation with deterministic inference using a delta posterior"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "preventing posterior collapse with delta vaes"
    - "non monotonic sequential text generation"
    - "flowseq non autoregressive conditional sequence generation with generative flow"
    - "image transformer"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"

Connected through hyperedge HE-3 (induced by source paper "implicit deep latent variable models for text generation"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "neural approaches to conversational ai"
    - "inference suboptimality in variational autoencoders"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "adversarial symmetric variational autoencoder"

Connected through hyperedge HE-4 (induced by source paper "semi supervised stochastic multi domain learning using variational inference"):
  with members:
    - "automatic language identification in texts a survey"
    - "implicit reparameterization gradients"
    - "auto encoding variational neural machine translation"
    - "multi space variational encoder decoders for semi supervised labeled sequence transduction"
    - "fixing a broken elbo"
    - "discovering discrete latent topics with neural variational inference"
    - "variational neural machine translation"
    - "generating sentences from a continuous space"

Connected through hyperedge HE-5 (induced by source paper "query the model precomputations for efficient inference with bayesian networks"):
  with members:
    - "a simple cache model for image recognition"
    - "database learning toward a database that becomes smarter every time"
    - "d separation from theorems to algorithms"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-6 (induced by source paper "auto encoding variational neural machine translation"):
  with members:
    - "a survey of domain adaptation for neural machine translation"
    - "spherical latent spaces for stable variational autoencoders"
    - "a call for clarity in reporting bleu scores"
    - "joint training for neural machine translation models with monolingual data"
    - "explaining and generalizing back translation through wake sleep"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "fixing a broken elbo"
    - "visualisation and diagnostic classifiers reveal how recurrent and recursive neural networks process hierarchical structure"

Connected through hyperedge HE-7 (induced by source paper "latent variable model for multi modal translation"):
  with members:
    - "auto encoding variational neural machine translation"
    - "imagination improves multimodal translation"
    - "opennmt open source toolkit for neural machine translation"
    - "findings of the second shared task on multimodal machine translation and multilingual image description"
    - "doubly attentive decoder for multi modal neural machine translation"
    - "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space"
    - "attention strategies for multi source sequence to sequence learning"
    - "neural machine translation with latent semantic of image and text"

Question: Please tell me which class the center node 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)