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: "diagonal rnns in symbolic music modeling"
  Abstract: In this paper, we propose a new Recurrent Neural Network (RNN) architecture. The novelty is simple: We use diagonal recurrent matrices instead of full. This results in better test likelihood and faster convergence compared to regular full RNNs in most of our experiments. We show the benefits of using diagonal recurrent matrices with popularly used LSTM and GRU architectures as well as with the vanilla RNN architecture, on four standard symbolic music datasets.

Connected through hyperedge HE-1 (induced by source paper "rethinking full connectivity in recurrent neural networks"):
  with members:
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "sparse persistent rnns squeezing large recurrent networks on chip"
    - "efficient neural audio synthesis"
    - "an analysis of neural language modeling at multiple scales"
    - "predefined sparseness in recurrent sequence models"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "block sparse recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"):
  with members:
    - "lstm a search space odyssey"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "on the state of the art of evaluation in neural language models"
    - "dilated recurrent neural networks"
    - "regularizing and optimizing lstm language models"
    - "comparative study of cnn and rnn for natural language processing"
    - "skip rnn learning to skip state updates in recurrent neural networks"
    - "temporal convolutional networks for action segmentation and detection"

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)