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 long short term memory recurrent neural networks part iii"
  Abstract: This is part III of three-part work. In parts I and II, we have presented eight variants for simplified Long Short Term Memory (LSTM) recurrent neural networks (RNNs). It is noted that fast computation, specially in constrained computing resources, are an important factor in processing big time-sequence data. In this part III paper, we present and evaluate two new LSTM model variants which dramatically reduce the computational load while retaining comparable performance to the base (standard) LSTM RNNs. In these new variants, we impose (Hadamard) pointwise state multiplications in the cell-memory network in addition to the gating signal networks.

Connected through hyperedge HE-1 (induced by source paper "slim lstm networks lstm_6 and lstm_c6"):
  with members:
    - "performance of three slim variants of the long short term memory lstm layer"
    - "slim lstms"
    - "lstm a search space odyssey"
    - "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"

Connected through hyperedge HE-2 (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 gating in long short term memory lstm recurrent neural networks"
    - "gate variants of gated recurrent unit gru neural networks"
    - "simplified minimal gated unit variations for recurrent 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-3 (induced by source paper "slim lstms"):
  with members:
    - "lstm a search space odyssey"
    - "simplified gating in long short term memory lstm recurrent neural networks"
    - "simplified minimal gated unit variations for 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)