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: "topically driven neural language model"
  Abstract: Language models are typically applied at the sentence level, without access to the broader document context. We present a neural language model that incorporates document context in the form of a topic model-like architecture, thus providing a succinct representation of the broader document context outside of the current sentence. Experiments over a range of datasets demonstrate that our model outperforms a pure sentence-based model in terms of language model perplexity, and leads to topics that are potentially more coherent than those produced by a standard LDA topic model. Our model also has the ability to generate related sentences for a topic, providing another way to interpret topics.

Connected through hyperedge HE-1 (induced by source paper "variational hetero encoder randomized gans for joint image text modeling"):
  with members:
    - "object driven text to image synthesis via adversarial training"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "text adaptive generative adversarial networks manipulating images with natural language"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "recent advances in zero shot recognition"
    - "self supervised learning of visual features through embedding images into text topic spaces"

Connected through hyperedge HE-2 (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-3 (induced by source paper "recurrent hierarchical topic guided neural language models"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "topic guided variational autoencoders for text generation"
    - "bottom up abstractive summarization"
    - "dirichlet belief networks for topic structure learning"
    - "topic compositional neural language model"
    - "attention is all you need"
    - "a neural knowledge language model"
    - "discovering discrete latent topics with neural variational inference"

Connected through hyperedge HE-4 (induced by source paper "variational hetero encoder randomized generative adversarial networks for joint image text modeling"):
  with members:
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "recent advances in zero shot recognition"
    - "self supervised learning of visual features through embedding images into text topic spaces"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "flow gan combining maximum likelihood and adversarial learning in generative models"
    - "less is more zero shot learning from online textual documents with noise suppression"
    - "improved techniques for training gans"

Connected through hyperedge HE-5 (induced by source paper "hierarchical context enabled recurrent neural network for recommendation"):
  with members:
    - "towards binary valued gates for robust lstm training"
    - "contextual sequence modeling for recommendation with recurrent neural networks"
    - "attention is all you need"
    - "discovering discrete latent topics with neural variational inference"
    - "phased lstm accelerating recurrent network training for long or event based sequences"
    - "improved recurrent neural networks for session based recommendations"
    - "topicrnn a recurrent neural network with long range semantic dependency"
    - "session based recommendations with recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "topiceq a joint topic and mathematical equation model for scientific texts"):
  with members:
    - "topic compositional neural language model"
    - "a neural knowledge language model"
    - "discovering discrete latent topics with neural variational inference"
    - "equation parsing mapping sentences to grounded equations"
    - "topicrnn a recurrent neural network with long range semantic dependency"
    - "image to markup generation with coarse to fine attention"
    - "exploring the limits of language modeling"
    - "neural variational inference for text processing"

Connected through hyperedge HE-7 (induced by source paper "stuck no worries task aware command recommendation and proactive help for analysts"):
  with members:
    - "intent aware contextual recommendation system"
    - "learning to attend copy and generate for session based query suggestion"
    - "topicrnn a recurrent neural network with long range semantic dependency"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"
    - "the lumiere project bayesian user modeling for inferring the goals and needs of software users"

Connected through hyperedge HE-8 (induced by source paper "topical phrase extraction from clinical reports by incorporating both local and global context"):
  with members:
    - "improving topic models with latent feature word representations"
    - "unsupervised learning of word sequence representations from scratch via convolutional tensor decomposition"
    - "topicrnn a recurrent neural network with long range semantic dependency"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

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)