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: "long short term memory based recurrent neural network architectures for large vocabulary speech recognition"
  Abstract: Long Short-Term Memory (LSTM) is a recurrent neural network (RNN) architecture that has been designed to address the vanishing and exploding gradient problems of conventional RNNs. Unlike feedforward neural networks, RNNs have cyclic connections making them powerful for modeling sequences. They have been successfully used for sequence labeling and sequence prediction tasks, such as handwriting recognition, language modeling, phonetic labeling of acoustic frames. However, in contrast to the deep neural networks, the use of RNNs in speech recognition has been limited to phone recognition in small scale tasks. In this paper, we present novel LSTM based RNN architectures which make more effective use of model parameters to train acoustic models for large vocabulary speech recognition. We train and compare LSTM, RNN and DNN models at various numbers of parameters and configurations. We show that LSTM models converge quickly and give state of the art speech recognition performance for relatively small sized models.

Connected through hyperedge HE-1 (induced by source paper "few shot deep adversarial learning for video based person re identification"):
  with members:
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "crossing generative adversarial networks for cross view person re identification"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "person re identification via recurrent feature aggregation"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"

Connected through hyperedge HE-2 (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-3 (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-4 (induced by source paper "generative models for effective ml on private decentralized datasets"):
  with members:
    - "dp cgan differentially private synthetic data and label generation"
    - "taxonomy of real faults in deep learning systems"
    - "reconstruction and membership inference attacks against generative models"
    - "towards federated learning at scale system design"
    - "differentially private generative adversarial networks for time series continuous and discrete open data"
    - "evaluating differentially private machine learning in practice"
    - "videoflow a flow based generative model for video"
    - "detecting bias with generative counterfactual face attribute augmentation"

Connected through hyperedge HE-5 (induced by source paper "software engineering meets deep learning a literature review"):
  with members:
    - "feature maps a comprehensible software representation for design pattern detection"
    - "on learning meaningful code changes via neural machine translation"
    - "on using machine learning to identify knowledge in api reference documentation"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "when deep learning met code search"
    - "a comprehensive study on deep learning bug characteristics"
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
    - "deepgauge multi granularity testing criteria for deep learning systems"

Connected through hyperedge HE-6 (induced by source paper "beyond human level accuracy computational challenges in deep learning"):
  with members:
    - "large scale language modeling converging on 40gb of text in four hours"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "imagenet training in minutes"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "exploring neural transducers for end to end speech recognition"
    - "nearly tight vc dimension and pseudodimension bounds for piecewise linear neural networks"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"

Connected through hyperedge HE-7 (induced by source paper "finetext text classification via attention based language model fine tuning"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "learning to generate reviews and discovering sentiment"
    - "selective encoding for abstractive sentence summarization"
    - "attention is all you need"
    - "learned in translation contextualized word vectors"
    - "semi supervised sequence tagging with bidirectional language models"

Connected through hyperedge HE-8 (induced by source paper "a morpho syntactically informed lstm crf model for named entity recognition"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "application of a hybrid bi lstm crf model to the task of russian named entity recognition"

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.NE(Neural and Evolutionary Computing)