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: "cached long short term memory neural networks for document level sentiment classification"
  Abstract: Recently, neural networks have achieved great success on sentiment classification due to their ability to alleviate feature engineering. However, one of the remaining challenges is to model long texts in document-level sentiment classification under a recurrent architecture because of the deficiency of the memory unit. To address this problem, we present a Cached Long Short-Term Memory neural networks (CLSTM) to capture the overall semantic information in long texts. CLSTM introduces a cache mechanism, which divides memory into several groups with different forgetting rates and thus enables the network to keep sentiment information better within a recurrent unit. The proposed CLSTM outperforms the state-of-the-art models on three publicly available document-level sentiment analysis datasets.

Connected through hyperedge HE-1 (induced by source paper "time aware large kernel convolutions"):
  with members:
    - "accelerating large kernel convolution using summed area tables"
    - "pay less attention with lightweight and dynamic convolutions"
    - "transformer xl attentive language models beyond a fixed length context"
    - "massively multilingual neural machine translation"
    - "fast parametric learning with activation memorization"
    - "weighted transformer network for machine translation"
    - "self attention with relative position representations"
    - "why self attention a targeted evaluation of neural machine translation architectures"

Connected through hyperedge HE-2 (induced by source paper "a precisely xtreme multi channel hybrid approach for roman urdu sentiment analysis"):
  with members:
    - "from web crawled text to project descriptions automatic summarizing of social innovation projects"
    - "an analysis of hierarchical text classification using word embeddings"
    - "a survey of the recent architectures of deep convolutional neural networks"
    - "character n gram embeddings to improve rnn language models"
    - "word class embeddings for multiclass text classification"
    - "using text analysis to quantify the similarity and evolution of scientific disciplines"
    - "sentiment classification of customer s reviews about automobiles in roman urdu"
    - "dynamic word embeddings for evolving semantic discovery"

Connected through hyperedge HE-3 (induced by source paper "riemannian normalizing flow on variational wasserstein autoencoder for text modeling"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "spherical latent spaces for stable variational autoencoders"
    - "auto encoding total correlation explanation"
    - "dirichlet variational autoencoder for text modeling"
    - "style transfer from non parallel text by cross alignment"
    - "a hybrid convolutional variational autoencoder for text generation"

Connected through hyperedge HE-4 (induced by source paper "neural gaussian copula for variational autoencoder"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "riemannian normalizing flow on variational wasserstein autoencoder for text modeling"
    - "spherical latent spaces for stable variational autoencoders"
    - "dirichlet variational autoencoder for text modeling"
    - "style transfer from non parallel text by cross alignment"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "generating sentences from a continuous space"

Connected through hyperedge HE-5 (induced by source paper "happy together learning and understanding appraisal from natural language"):
  with members:
    - "happydb a corpus of 100 000 crowdsourced happy moments"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "universal language model fine tuning for text classification"
    - "learned in translation contextualized word vectors"
    - "recurrent neural network for text classification with multi task learning"
    - "improved semantic representations from tree structured long short term memory networks"
    - "one billion word benchmark for measuring progress in statistical language modeling"

Connected through hyperedge HE-6 (induced by source paper "deep learning for sentiment analysis a survey"):
  with members:
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "tensor fusion network for multimodal sentiment analysis"
    - "sarcasm sign interpreting sarcasm with sentiment based monolingual machine translation"
    - "volatility prediction using financial disclosures sentiments with word embedding based ir models"
    - "deep memory networks for attitude identification"
    - "interactive attention networks for aspect level sentiment classification"
    - "recursive neural conditional random fields for aspect based sentiment analysis"
    - "a deeper look into sarcastic tweets using deep convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "dual memory network model for biased product review classification"):
  with members:
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "leveraging cognitive features for sentiment analysis"
    - "linguistically regularized lstms for sentiment classification"
    - "aspect level sentiment classification with deep memory network"
    - "end to end memory networks"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-8 (induced by source paper "tree memory networks for modelling long term temporal dependencies"):
  with members:
    - "tracking by prediction a deep generative model for mutli person localisation and tracking"
    - "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"
    - "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"
    - "interpretable structure evolving lstm"
    - "two stream lstm a deep fusion framework for human action recognition"
    - "semantic object parsing with graph lstm"
    - "enhancing and combining sequential and tree lstm for natural language inference"
    - "enhanced lstm for natural language inference"

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)