human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "slim lstms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "slim lstm networks lstm_6 and lstm_c6")
  Member "lstm a search space odyssey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "simplified long short term memory recurrent neural networks part iii":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "slim lstm networks lstm_6 and lstm_c6")
  Member "simplified gating in long short term memory lstm recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "slim lstm networks lstm_6 and lstm_c6")
  Member "gate variants of gated recurrent unit gru neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spatio temporal pyramid graph convolutions for human action recognition and postural assessment")
  Member "simplified minimal gated unit variations for recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reduced gate convolutional lstm using predictive coding for spatiotemporal prediction")
  Member "long short term memory based recurrent neural network architectures for large vocabulary speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "few shot deep adversarial learning for video based person re identification")
  Member "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "music generation by deep learning challenges and directions")

Question: Please tell me which class the source paper 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)