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: "going wider recurrent neural network with parallel cells"
  Abstract: Recurrent Neural Network (RNN) has been widely applied for sequence modeling. In RNN, the hidden states at current step are full connected to those at previous step, thus the influence from less related features at previous step may potentially decrease model's learning ability. We propose a simple technique called parallel cells (PCs) to enhance the learning ability of Recurrent Neural Network (RNN). In each layer, we run multiple small RNN cells rather than one single large cell. In this paper, we evaluate PCs on 2 tasks. On language modeling task on PTB (Penn Tree Bank), our model outperforms state of art models by decreasing perplexity from 78.6 to 75.3. On Chinese-English translation task, our model increases BLEU score for 0.39 points than baseline model.

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 "thick net parallel network structure for sequential modeling"):
  with members:
    - "on the state of the art of evaluation in neural language models"
    - "regularizing and optimizing lstm language models"
    - "skip rnn learning to skip state updates in recurrent neural networks"
    - "linguistically regularized lstms for sentiment classification"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "recurrent dropout without memory loss"
    - "recurrent highway networks"
    - "improving neural language models with a continuous cache"

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.CL(Computation and Language)