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: "high order recurrent neural networks for acoustic modelling"
  Abstract: Vanishing long-term gradients are a major issue in training standard recurrent neural networks (RNNs), which can be alleviated by long short-term memory (LSTM) models with memory cells. However, the extra parameters associated with the memory cells mean an LSTM layer has four times as many parameters as an RNN with the same hidden vector size. This paper addresses the vanishing gradient problem using a high order RNN (HORNN) which has additional connections from multiple previous time steps. Speech recognition experiments using British English multi-genre broadcast (MGB3) data showed that the proposed HORNN architectures for rectified linear unit and sigmoid activation functions reduced word error rates (WER) by 4.2% and 6.3% over the corresponding RNNs, and gave similar WERs to a (projected) LSTM while using only 20%--50% of the recurrent layer parameters and computation.

Connected through hyperedge HE-1 (induced by source paper "non local recurrent neural memory for supervised sequence modeling"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "adding attentiveness to the neurons in recurrent neural networks"
    - "relational recurrent neural networks"
    - "glomo unsupervisedly learned relational graphs as transferable representations"
    - "learning to generate reviews and discovering sentiment"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "non local neural networks"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "semi tied units for efficient gating in lstm and highway networks"):
  with members:
    - "improved tdnns using deep kernels and frequency dependent grid rnns"
    - "very deep convolutional networks for end to end speech recognition"
    - "quasi recurrent neural networks"
    - "learning hidden unit contributions for unsupervised acoustic model adaptation"
    - "fast and accurate recurrent neural network acoustic models for speech recognition"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "small footprint deep neural networks with highway connections for speech recognition"
    - "highway long short term memory rnns for distant speech recognition"

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)