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: "simple recurrent units for highly parallelizable recurrence"
  Abstract: Common recurrent neural architectures scale poorly due to the intrinsic difficulty in parallelizing their state computations. In this work, we propose the Simple Recurrent Unit (SRU), a light recurrent unit that balances model capacity and scalability. SRU is designed to provide expressive recurrence, enable highly parallelized implementation, and comes with careful initialization to facilitate training of deep models. We demonstrate the effectiveness of SRU on multiple NLP tasks. SRU achieves 5--9x speed-up over cuDNN-optimized LSTM on classification and question answering datasets, and delivers stronger results than LSTM and convolutional models. We also obtain an average of 0.7 BLEU improvement over the Transformer model on translation by incorporating SRU into the architecture.

Connected through hyperedge HE-1 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units"):
  with members:
    - "towards non saturating recurrent units for modelling long term dependencies"
    - "what is wrong with scene text recognition model comparisons dataset and model analysis"
    - "non normal recurrent neural network nnrnn learning long time dependencies while improving expressivity with transient dynamics"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "lstm a search space odyssey"
    - "a flexible approach to automated rnn architecture generation"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"
    - "focusing attention towards accurate text recognition in natural images"

Connected through hyperedge HE-2 (induced by source paper "mhsan multi head self attention network for visual semantic embedding"):
  with members:
    - "univse robust visual semantic embeddings via structured semantic representations"
    - "finding beans in burgers deep semantic visual embedding with localization"
    - "bam bottleneck attention module"
    - "stacked cross attention for image text matching"
    - "cbam convolutional block attention module"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "learning two branch neural networks for image text matching tasks"
    - "attention is all you need"

Connected through hyperedge HE-3 (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-4 (induced by source paper "read beyond the lines understanding the implied textual meaning via a skim and intensive reading model"):
  with members:
    - "detect camouflaged spam content via stoneskipping graph and text joint embedding for chinese character variation representation"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "attention is all you need"
    - "a large self annotated corpus for sarcasm"
    - "convolutional sequence to sequence learning"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"

Connected through hyperedge HE-5 (induced by source paper "learn to generate time series conditioned graphs with generative adversarial nets"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "labeled graph generative adversarial networks"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"
    - "deep graph translation"
    - "junction tree variational autoencoder for molecular graph generation"
    - "instance normalization the missing ingredient for fast stylization"

Connected through hyperedge HE-6 (induced by source paper "deep independently recurrent neural network indrnn"):
  with members:
    - "temporal transformer networks joint learning of invariant and discriminative time warping"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "part based graph convolutional network for action recognition"
    - "learning long term dependencies via fourier recurrent units"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "view adaptive neural networks for high performance skeleton based human action recognition"
    - "lstm a search space odyssey"
    - "breaking the softmax bottleneck a high rank rnn language model"

Connected through hyperedge HE-7 (induced by source paper "efficient contextual representation learning without softmax layer"):
  with members:
    - "von mises fisher loss for training sequence to sequence models with continuous outputs"
    - "multilingual constituency parsing with self attention and pre training"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "an analysis of neural language modeling at multiple scales"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dissecting contextual word embeddings architecture and representation"

Connected through hyperedge HE-8 (induced by source paper "define deep factorized input word embeddings for neural sequence modeling"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "deep contextualized word representations"
    - "an analysis of neural language modeling at multiple scales"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "groupreduce block wise low rank approximation for neural language model shrinking"
    - "online embedding compression for text classification using low rank matrix factorization"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "adaptive input representations for neural language modeling"

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)