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: "residual lstm design of a deep recurrent architecture for distant speech recognition"
  Abstract: In this paper, a novel architecture for a deep recurrent neural network, residual LSTM is introduced. A plain LSTM has an internal memory cell that can learn long term dependencies of sequential data. It also provides a temporal shortcut path to avoid vanishing or exploding gradients in the temporal domain. The residual LSTM provides an additional spatial shortcut path from lower layers for efficient training of deep networks with multiple LSTM layers. Compared with the previous work, highway LSTM, residual LSTM separates a spatial shortcut path with temporal one by using output layers, which can help to avoid a conflict between spatial and temporal-domain gradient flows. Furthermore, residual LSTM reuses the output projection matrix and the output gate of LSTM to control the spatial information flow instead of additional gate networks, which effectively reduces more than 10% of network parameters. An experiment for distant speech recognition on the AMI SDM corpus shows that 10-layer plain and highway LSTM networks presented 13.7% and 6.2% increase in WER over 3-layer aselines, respectively. On the contrary, 10-layer residual LSTM networks provided the lowest WER 41.0%, which corresponds to 3.3% and 2.8% WER reduction over plain and highway LSTM networks, respectively.

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"
    - "simple recurrent units for highly parallelizable recurrence"
    - "lstm a search space odyssey"
    - "a flexible approach to automated rnn architecture generation"
    - "focusing attention towards accurate text recognition in natural images"

Connected through hyperedge HE-2 (induced by source paper "steerage synthesis of neural networks using architecture search and grow and prune methods"):
  with members:
    - "incremental learning using a grow and prune paradigm with efficient neural networks"
    - "scann synthesis of compact and accurate neural networks"
    - "single path one shot neural architecture search with uniform sampling"
    - "switchable normalization for learning to normalize deep representation"
    - "secret semantically enhanced classification of real world tasks"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "amc automl for model compression and acceleration on mobile devices"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"

Connected through hyperedge HE-3 (induced by source paper "gating revisited deep multi layer rnns that can be trained"):
  with members:
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "can recurrent neural networks warp time"
    - "the unreasonable effectiveness of the forget gate"
    - "multi temporal land cover classification with sequential recurrent encoders"
    - "skip rnn learning to skip state updates in recurrent neural networks"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "efficient orthogonal parametrisation of recurrent neural networks using householder reflections"

Connected through hyperedge HE-4 (induced by source paper "discrete and continuous deep residual learning over graphs"):
  with members:
    - "recurrent event network for reasoning over temporal knowledge graphs"
    - "a comprehensive survey on graph neural networks"
    - "group normalization"
    - "learning a sat solver from single bit supervision"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "relational inductive biases deep learning and graph networks"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"

Connected through hyperedge HE-5 (induced by source paper "improving rnn transducer modeling for end to end speech recognition"):
  with members:
    - "advancing acoustic to word ctc model with attention and mixed units"
    - "advancing acoustic to word ctc model"
    - "layer trajectory lstm"
    - "advancing connectionist temporal classification with attention modeling"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "monotonic chunkwise attention"
    - "exploring neural transducers for end to end speech recognition"
    - "improving the performance of online neural transducer models"

Connected through hyperedge HE-6 (induced by source paper "federated learning of n gram language models"):
  with members:
    - "approximating probabilistic models as weighted finite automata"
    - "federated learning strategies for improving communication efficiency"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "federated learning for mobile keyboard prediction"
    - "lstm a search space odyssey"
    - "state of the art speech recognition with sequence to sequence models"
    - "learning differentially private recurrent language models"
    - "factorization tricks for lstm networks"

Connected through hyperedge HE-7 (induced by source paper "two stage training for chinese dialect recognition"):
  with members:
    - "training multi task adversarial network for extracting noise robust speaker embedding"
    - "speaker embedding extraction with phonetic information"
    - "phonetic temporal neural model for language identification"
    - "residual convolutional ctc networks for automatic speech recognition"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "deep residual learning for image recognition"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "n beats neural basis expansion analysis for interpretable time series forecasting"):
  with members:
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"
    - "dilated recurrent neural networks"
    - "xgboost a scalable tree boosting system"
    - "deep residual learning for image 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.LG(Machine Learning)