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: "variational recurrent neural machine translation"
  Abstract: Partially inspired by successful applications of variational recurrent neural networks, we propose a novel variational recurrent neural machine translation (VRNMT) model in this paper. Different from the variational NMT, VRNMT introduces a series of latent random variables to model the translation procedure of a sentence in a generative way, instead of a single latent variable. Specifically, the latent random variables are included into the hidden states of the NMT decoder with elements from the variational autoencoder. In this way, these variables are recurrently generated, which enables them to further capture strong and complex dependencies among the output translations at different timesteps. In order to deal with the challenges in performing efficient posterior inference and large-scale training during the incorporation of latent variables, we build a neural posterior approximator, and equip it with a reparameterization technique to estimate the variational lower bound. Experiments on Chinese-English and English-German translation tasks demonstrate that the proposed model achieves significant improvements over both the conventional and variational NMT models.

Connected through hyperedge HE-1 (induced by source paper "mixture content selection for diverse sequence generation"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "mixture models for diverse machine translation tricks of the trade"
    - "deep communicating agents for abstractive summarization"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "bottom up abstractive summarization"
    - "a hierarchical latent structure for variational conversation modeling"
    - "hierarchical neural story generation"
    - "spherical latent spaces for stable variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "improved variational neural machine translation by promoting mutual information"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "preventing posterior collapse with delta vaes"
    - "mixture models for diverse machine translation tricks of the trade"
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "scaling neural machine translation"
    - "bag of words as target for neural machine translation"
    - "a call for clarity in reporting bleu scores"
    - "mtnt a testbed for machine translation of noisy text"

Connected through hyperedge HE-3 (induced by source paper "modeling event background for if then commonsense reasoning using context aware variational autoencoder"):
  with members:
    - "topic guided variational autoencoders for text generation"
    - "story ending generation with incremental encoding and commonsense knowledge"
    - "hierarchical neural story generation"
    - "constructing narrative event evolutionary graph for script event prediction"
    - "event2mind commonsense inference on events intents and reactions"
    - "atomic an atlas of machine commonsense for if then reasoning"
    - "natural language inference over interaction space"
    - "latent intention dialogue models"

Connected through hyperedge HE-4 (induced by source paper "neural simile recognition with cyclic multitask learning and local attention"):
  with members:
    - "open domain targeted sentiment analysis via span based extraction and classification"
    - "a unified model for opinion target extraction and target sentiment prediction"
    - "modeling localness for self attention networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "self attentional acoustic models"
    - "variational neural discourse relation recognizer"
    - "variational neural machine translation"
    - "recurrent neural network for text classification with multi task learning"

Connected through hyperedge HE-5 (induced by source paper "simplifying neural machine translation with addition subtraction twin gated recurrent networks"):
  with members:
    - "asynchronous bidirectional decoding for neural machine translation"
    - "accelerating neural transformer via an average attention network"
    - "recurrent additive networks"
    - "attention is all you need"
    - "a gru gated attention model for neural machine translation"
    - "modeling past and future for neural machine translation"
    - "convolutional sequence to sequence learning"
    - "deep neural machine translation with linear associative unit"

Connected through hyperedge HE-6 (induced by source paper "refining source representations with relation networks for neural machine translation"):
  with members:
    - "learning to parse and translate improves neural machine translation"
    - "attention is all you need"
    - "a simple neural network module for relational reasoning"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "towards string to tree neural machine translation"
    - "modeling source syntax for neural machine translation"
    - "convolutional sequence to sequence learning"
    - "densely connected convolutional networks"

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)