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: "bayesian compression for natural language processing"
  Abstract: In natural language processing, a lot of the tasks are successfully solved with recurrent neural networks, but such models have a huge number of parameters. The majority of these parameters are often concentrated in the embedding layer, which size grows proportionally to the vocabulary length. We propose a Bayesian sparsification technique for RNNs which allows compressing the RNN dozens or hundreds of times without time-consuming hyperparameters tuning. We also generalize the model for vocabulary sparsification to filter out unnecessary words and compress the RNN even further. We show that the choice of the kept words is interpretable.

Connected through hyperedge HE-1 (induced by source paper "anchor transform learning sparse representations of discrete objects"):
  with members:
    - "neural input search for large scale recommendation models"
    - "how large a vocabulary does text classification need a variational approach to vocabulary selection"
    - "word embedding based on low rank doubly stochastic matrix decomposition"
    - "probabilistic fasttext for multi sense word embeddings"
    - "adaptive input representations for neural language modeling"
    - "towards learning sparsely used dictionaries with arbitrary supports"
    - "attention is all you need"
    - "learning k way d dimensional discrete code for compact embedding representations"

Connected through hyperedge HE-2 (induced by source paper "compression of recurrent neural networks for efficient language modeling"):
  with members:
    - "trellis networks for sequence modeling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "online embedding compression for text classification using low rank matrix factorization"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"
    - "compressing recurrent neural network with tensor train"
    - "pointing the unknown words"

Connected through hyperedge HE-3 (induced by source paper "how large a vocabulary does text classification need a variational approach to vocabulary selection"):
  with members:
    - "compressing recurrent neural network with tensor train"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "character based neural machine translation"
    - "learning structured sparsity in deep neural networks"
    - "enhanced lstm for natural language inference"
    - "fully character level neural machine translation without explicit segmentation"
    - "faster cnns with direct sparse convolutions and guided pruning"
    - "quantized neural networks training neural networks with low precision weights and activations"

Connected through hyperedge HE-4 (induced by source paper "restricted recurrent neural networks"):
  with members:
    - "compression of recurrent neural networks for efficient language modeling"
    - "performance of three slim variants of the long short term memory lstm layer"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "lstm a search space odyssey"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "regularizing and optimizing lstm language models"
    - "compressing recurrent neural network with tensor train"
    - "condensenet an efficient densenet using learned group convolutions"

Connected through hyperedge HE-5 (induced by source paper "structured sparsification of gated recurrent neural networks"):
  with members:
    - "exploring sparsity in recurrent neural networks"
    - "compression of neural machine translation models via pruning"
    - "adam a method for stochastic optimization"
    - "recurrent neural network regularization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "a variational dirichlet framework for out of distribution detection"):
  with members:
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "evidential deep learning to quantify classification uncertainty"
    - "toward controlled generation of text"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "variational reasoning for question answering with knowledge graph"
    - "attention is all you need"
    - "learning from noisy labels with distillation"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "bayesian sparsification of gated recurrent neural networks"):
  with members:
    - "learning intrinsic sparse structures within long short term memory"
    - "compressing recurrent neural network with tensor train"
    - "exploring sparsity in recurrent neural networks"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "exploring models and data for image question answering"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "adam a method for stochastic optimization"
    - "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)