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: "simplified minimal gated unit variations for recurrent neural networks"
  Abstract: Recurrent neural networks with various types of hidden units have been used to solve a diverse range of problems involving sequence data. Two of the most recent proposals, gated recurrent units (GRU) and minimal gated units (MGU), have shown comparable promising results on example public datasets. In this paper, we introduce three model variants of the minimal gated unit (MGU) which further simplify that design by reducing the number of parameters in the forget-gate dynamic equation. These three model variants, referred to simply as MGU1, MGU2, and MGU3, were tested on sequences generated from the MNIST dataset and from the Reuters Newswire Topics (RNT) dataset. The new models have shown similar accuracy to the MGU model while using fewer parameters and thus lowering training expense. One model variant, namely MGU2, performed better than MGU on the datasets considered, and thus may be used as an alternate to MGU or GRU in recurrent neural networks.

Connected through hyperedge HE-1 (induced by source paper "performance of three slim variants of the long short term memory lstm layer"):
  with members:
    - "slim lstms"
    - "lstm a search space odyssey"
    - "simplified long short term memory recurrent neural networks part iii"
    - "simplified gating in long short term memory lstm recurrent neural networks"
    - "gate variants of gated recurrent unit gru neural networks"
    - "long short term memory based recurrent neural network architectures for large vocabulary speech recognition"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"

Connected through hyperedge HE-2 (induced by source paper "reduced gate convolutional lstm using predictive coding for spatiotemporal prediction"):
  with members:
    - "deep gated recurrent and convolutional network hybrid model for univariate time series classification"
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "lstm a search space odyssey"
    - "deep learning for precipitation nowcasting a benchmark and a new model"
    - "unsupervised learning for physical interaction through video prediction"
    - "noisy activation functions"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "minimal gated unit for recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "slim lstms"):
  with members:
    - "lstm a search space odyssey"
    - "simplified long short term memory recurrent neural networks part iii"
    - "simplified gating in long short term memory lstm recurrent neural networks"
    - "a basic recurrent neural network model"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "empirical evaluation of gated recurrent neural networks on sequence 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.NE(Neural and Evolutionary Computing)