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: "bidirectional lstm crf models for sequence tagging"
  Abstract: In this paper, we propose a variety of Long Short-Term Memory (LSTM) based models for sequence tagging. These models include LSTM networks, bidirectional LSTM (BI-LSTM) networks, LSTM with a Conditional Random Field (CRF) layer (LSTM-CRF) and bidirectional LSTM with a CRF layer (BI-LSTM-CRF). Our work is the first to apply a bidirectional LSTM CRF (denoted as BI-LSTM-CRF) model to NLP benchmark sequence tagging data sets. We show that the BI-LSTM-CRF model can efficiently use both past and future input features thanks to a bidirectional LSTM component. It can also use sentence level tag information thanks to a CRF layer. The BI-LSTM-CRF model can produce state of the art (or close to) accuracy on POS, chunking and NER data sets. In addition, it is robust and has less dependence on word embedding as compared to previous observations.

Connected through hyperedge HE-1 (induced by source paper "dense dilated convolutions merging network for land cover classification"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "dense fusion classmate network for land cover classification"
    - "on the convergence of adam and beyond"
    - "dense dilated convolutions merging network for semantic mapping of remote sensing images"
    - "sdc stacked dilated convolution a unified descriptor network for dense matching tasks"
    - "uncertainty gated network for land cover segmentation"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "urban land cover classification with missing data modalities using deep convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "training simplification and model simplification for deep learning a minimal effort back propagation method"):
  with members:
    - "bag of words as target for neural machine translation"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "neural system combination for machine translation"
    - "attention is all you need"
    - "nonparametric neural networks"
    - "sparse communication for distributed gradient descent"
    - "meprop sparsified back propagation for accelerated deep learning with reduced overfitting"
    - "exploring sparsity in recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "variational template machine for data to text generation"):
  with members:
    - "key fact as pivot a two stage model for low resource table to text generation"
    - "using monolingual data in neural machine translation a systematic study"
    - "the curious case of neural text degeneration"
    - "table to text generation with effective hierarchical encoder on three dimensions row column and time"
    - "generating sentences from disentangled syntactic and semantic spaces"
    - "disentangled representation learning for non parallel text style transfer"
    - "can neural generators for dialogue learn sentence planning and discourse structuring"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"

Connected through hyperedge HE-4 (induced by source paper "arabert transformer based model for arabic language understanding"):
  with members:
    - "towards a human like open domain chatbot"
    - "neural arabic question answering"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "unsupervised cross lingual representation learning at scale"
    - "camembert a tasty french language model"
    - "albert a lite bert for self supervised learning of language representations"
    - "arsentd lev a multi topic corpus for target based sentiment analysis in arabic levantine tweets"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-5 (induced by source paper "don t parse generate a sequence to sequence architecture for task oriented semantic parsing"):
  with members:
    - "improving semantic parsing for task oriented dialog"
    - "roberta a robustly optimized bert pretraining approach"
    - "joint slot filling and intent detection via capsule neural networks"
    - "exploring neural methods for parsing discourse representation structures"
    - "style transfer through back translation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"

Connected through hyperedge HE-6 (induced by source paper "trans blstm transformer with bidirectional lstm for language understanding"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "spanbert improving pre training by representing and predicting spans"
    - "multi task deep neural networks for natural language understanding"
    - "albert a lite bert for self supervised learning of language representations"
    - "distilling task specific knowledge from bert into simple neural networks"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "patient knowledge distillation for bert model compression"

Connected through hyperedge HE-7 (induced by source paper "hybrid neural tagging model for open relation extraction"):
  with members:
    - "an open world extension to knowledge graph completion models"
    - "graph neural networks with generated parameters for relation extraction"
    - "openki integrating open information extraction and knowledge bases with relation inference"
    - "design challenges and misconceptions in neural sequence labeling"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "a survey on open information extraction"
    - "neural open information extraction"
    - "deep active learning for named entity recognition"

Connected through hyperedge HE-8 (induced by source paper "utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "detection of the prodromal phase of bipolar disorder from psychological and phonological aspects in social media"
    - "advances in pre training distributed word representations"
    - "enriching word vectors with subword information"
    - "identifying depression on twitter"

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)