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: "improved variational autoencoders for text modeling using dilated convolutions"
  Abstract: Recent work on generative modeling of text has found that variational auto-encoders (VAE) incorporating LSTM decoders perform worse than simpler LSTM language models (Bowman et al., 2015). This negative result is so far poorly understood, but has been attributed to the propensity of LSTM decoders to ignore conditioning information from the encoder. In this paper, we experiment with a new type of decoder for VAE: a dilated CNN. By changing the decoder's dilation architecture, we control the effective context from previously generated words. In experiments, we find that there is a trade off between the contextual capacity of the decoder and the amount of encoding information used. We show that with the right decoder, VAE can outperform LSTM language models. We demonstrate perplexity gains on two datasets, representing the first positive experimental result on the use VAE for generative modeling of text. Further, we conduct an in-depth investigation of the use of VAE (with our new decoding architecture) for semi-supervised and unsupervised labeling tasks, demonstrating gains over several strong baselines.

Connected through hyperedge HE-1 (induced by source paper "blank language models"):
  with members:
    - "tigs an inference algorithm for text infilling with gradient search"
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "synchronous bidirectional neural machine translation"
    - "restoring ancient text using deep learning a case study on greek epigraphy"
    - "mask predict parallel decoding of conditional masked language models"
    - "ctrl a conditional transformer language model for controllable generation"
    - "transformer xl attentive language models beyond a fixed length context"

Connected through hyperedge HE-2 (induced by source paper "modelling latent skills for multitask language generation"):
  with members:
    - "plug and play language models a simple approach to controlled text generation"
    - "guiding extractive summarization with question answering rewards"
    - "continual unsupervised representation learning"
    - "leafnats an open source toolkit and live demo system for neural abstractive text summarization"
    - "pegasus pre training with extracted gap sentences for abstractive summarization"
    - "putting machine translation in context with the noisy channel model"
    - "neural extractive text summarization with syntactic compression"
    - "text summarization with pretrained encoders"

Connected through hyperedge HE-3 (induced by source paper "sentencemim a latent variable language model"):
  with members:
    - "implicit deep latent variable models for text generation"
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "dynamic evaluation of transformer language models"
    - "preventing posterior collapse with delta vaes"
    - "a stable variational autoencoder for text modelling"
    - "attention is all you need"
    - "learning to rank question answer pairs with holographic dual lstm architecture"

Connected through hyperedge HE-4 (induced by source paper "characterizing and avoiding problematic global optima of variational autoencoders"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "preventing posterior collapse with delta vaes"
    - "constrained graph variational autoencoders for molecule design"
    - "infovae information maximizing variational autoencoders"
    - "towards deeper understanding of variational autoencoding models"
    - "vae with a vampprior"
    - "neural discrete representation learning"
    - "pixelvae a latent variable model for natural images"

Connected through hyperedge HE-5 (induced by source paper "an introduction to variational autoencoders"):
  with members:
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "isolating sources of disentanglement in variational autoencoders"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "bayesian recurrent neural networks"
    - "flow gan combining maximum likelihood and adversarial learning in generative models"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks"
    - "improving variational auto encoders using householder flow"

Connected through hyperedge HE-6 (induced by source paper "select and attend towards controllable content selection in text generation"):
  with members:
    - "toward unsupervised text content manipulation"
    - "towards content transfer through grounded text generation"
    - "step by step separating planning from realization in neural data to text generation"
    - "global encoding for abstractive summarization"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "bottom up abstractive summarization"
    - "show control and tell a framework for generating controllable and grounded captions"

Connected through hyperedge HE-7 (induced by source paper "topic guided variational autoencoders for text generation"):
  with members:
    - "sequence generation with guider network"
    - "a reinforced topic aware convolutional sequence to sequence model for abstractive text summarization"
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "texygen a benchmarking platform for text generation models"
    - "toward controlled generation of text"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "selective encoding for abstractive sentence summarization"

Connected through hyperedge HE-8 (induced by source paper "a cross sentence latent variable model for semi supervised text sequence matching"):
  with members:
    - "deep contextualized word representations"
    - "spherical latent spaces for stable variational autoencoders"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adventure adversarial training for textual entailment with knowledge guided examples"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "generating sentences by editing prototypes"
    - "neural natural language inference models enhanced with external knowledge"
    - "deconvolutional latent variable model for text sequence matching"

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.NE(Neural and Evolutionary Computing)