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: "depth gated lstm"
  Abstract: In this short note, we present an extension of long short-term memory (LSTM) neural networks to using a depth gate to connect memory cells of adjacent layers. Doing so introduces a linear dependence between lower and upper layer recurrent units. Importantly, the linear dependence is gated through a gating function, which we call depth gate. This gate is a function of the lower layer memory cell, the input to and the past memory cell of this layer. We conducted experiments and verified that this new architecture of LSTMs was able to improve machine translation and language modeling performances.

Connected through hyperedge HE-1 (induced by source paper "recurrent neural networks for time series forecasting current status and future directions"):
  with members:
    - "n beats neural basis expansion analysis for interpretable time series forecasting"
    - "sales demand forecast in e commerce using a long short term memory neural network methodology"
    - "gluonts probabilistic time series models in python"
    - "temporal pattern attention for multivariate time series forecasting"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "an overview and comparative analysis of recurrent neural networks for short term load forecasting"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"

Connected through hyperedge HE-2 (induced by source paper "recent advances in recurrent neural networks"):
  with members:
    - "gated orthogonal recurrent units on learning to forget"
    - "generalization of deep neural networks for chest pathology classification in x rays using generative adversarial networks"
    - "recurrent generative adversarial networks for proximal learning and automated compressive image recovery"
    - "interpretation of mammogram and chest radiograph reports using deep neural networks preliminary results"
    - "recurrent dropout without memory loss"
    - "recurrent memory network for language modeling"
    - "wavenet a generative model for raw audio"
    - "pixel recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "learning private neural language modeling with attentive aggregation"):
  with members:
    - "on first order meta learning algorithms"
    - "federated learning strategies for improving communication efficiency"
    - "federated meta learning for recommendation"
    - "federated meta learning with fast convergence and efficient communication"
    - "differentially private federated learning a client level perspective"
    - "federated tensor factorization for computational phenotyping"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-4 (induced by source paper "the unreasonable effectiveness of the forget gate"):
  with members:
    - "can recurrent neural networks warp time"
    - "lstm a search space odyssey"
    - "learning simpler language models with the differential state framework"
    - "dilated recurrent neural networks"
    - "phased lstm accelerating recurrent network training for long or event based sequences"
    - "minimal gated unit for recurrent neural networks"
    - "wavenet a generative model for raw audio"
    - "architectural complexity measures of recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "recurrent neural networks with flexible gates using kernel activation functions"):
  with members:
    - "can recurrent neural networks warp time"
    - "the unreasonable effectiveness of the forget gate"
    - "lstm a search space odyssey"
    - "deep gate recurrent neural network"
    - "minimal gated unit for recurrent neural networks"
    - "architectural complexity measures of recurrent neural networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "regularizing rnns by stabilizing activations"

Connected through hyperedge HE-6 (induced by source paper "shorten spatial spectral rnn with parallel gru for hyperspectral image classification"):
  with members:
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

Connected through hyperedge HE-7 (induced by source paper "topically driven neural language model"):
  with members:
    - "recurrent memory networks for language modeling"
    - "end to end memory networks"
    - "document context language models"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural turing machines"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "recurrent neural network regularization"

Connected through hyperedge HE-8 (induced by source paper "long short term memory networks for machine reading"):
  with members:
    - "learning to compose neural networks for question answering"
    - "recurrent memory network for language modeling"
    - "dynamic memory networks for visual and textual question answering"
    - "end to end memory networks"
    - "a neural attention model for abstractive sentence summarization"
    - "a deep memory based architecture for sequence to sequence learning"
    - "neural transformation machine a new architecture for sequence to sequence learning"
    - "transition based dependency parsing with stack long short term memory"

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)