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: "performance of three slim variants of the long short term memory lstm layer"
  Abstract: The Long Short-Term Memory (LSTM) layer is an important advancement in the field of neural networks and machine learning, allowing for effective training and impressive inference performance. LSTM-based neural networks have been successfully employed in various applications such as speech processing and language translation. The LSTM layer can be simplified by removing certain components, potentially speeding up training and runtime with limited change in performance. In particular, the recently introduced variants, called SLIM LSTMs, have shown success in initial experiments to support this view. Here, we perform computational analysis of the validation accuracy of a convolutional plus recurrent neural network architecture using comparatively the standard LSTM and three SLIM LSTM layers. We have found that some realizations of the SLIM LSTM layers can potentially perform as well as the standard LSTM layer for our considered architecture.

Connected through hyperedge HE-1 (induced by source paper "restricted recurrent neural networks"):
  with members:
    - "compression of recurrent neural networks for efficient language modeling"
    - "bayesian compression for natural language processing"
    - "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-2 (induced by source paper "slim lstm networks lstm_6 and lstm_c6"):
  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"
    - "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"
    - "recurrent neural network regularization"

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)