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: "improved memory in recurrent neural networks with sequential non normal dynamics"
  Abstract: Training recurrent neural networks (RNNs) is a hard problem due to degeneracies in the optimization landscape, a problem also known as vanishing/exploding gradients. Short of designing new RNN architectures, previous methods for dealing with this problem usually boil down to orthogonalization of the recurrent dynamics, either at initialization or during the entire training period. The basic motivation behind these methods is that orthogonal transformations are isometries of the Euclidean space, hence they preserve (Euclidean) norms and effectively deal with vanishing/exploding gradients. However, this ignores the crucial effects of non-linearity and noise. In the presence of a non-linearity, orthogonal transformations no longer preserve norms, suggesting that alternative transformations might be better suited to non-linear networks. Moreover, in the presence of noise, norm preservation itself ceases to be the ideal objective. A more sensible objective is maximizing the signal-to-noise ratio (SNR) of the propagated signal instead. Previous work has shown that in the linear case, recurrent networks that maximize the SNR display strongly non-normal, sequential dynamics and orthogonal networks are highly suboptimal by this measure. Motivated by this finding, here we investigate the potential of non-normal RNNs, i.e. RNNs with a non-normal recurrent connectivity matrix, in sequential processing tasks. Our experimental results show that non-normal RNNs outperform their orthogonal counterparts in a diverse range of benchmarks. We also find evidence for increased non-normality and hidden chain-like feedforward motifs in trained RNNs initialized with orthogonal recurrent connectivity matrices.

Connected through hyperedge HE-1 (induced by source paper "eigenvalue normalized recurrent neural networks for short term memory"):
  with members:
    - "cheap orthogonal constraints in neural networks a simple parametrization of the orthogonal and unitary group"
    - "non normal recurrent neural network nnrnn learning long time dependencies while improving expressivity with transient dynamics"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"
    - "complex gated recurrent neural networks"
    - "gated orthogonal recurrent units on learning to forget"
    - "fast slow recurrent neural networks"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "efficient orthogonal parametrisation of recurrent neural networks using householder reflections"

Connected through hyperedge HE-2 (induced by source paper "non normal recurrent neural network nnrnn learning long time dependencies while improving expressivity with transient dynamics"):
  with members:
    - "cheap orthogonal constraints in neural networks a simple parametrization of the orthogonal and unitary group"
    - "towards non saturating recurrent units for modelling long term dependencies"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"
    - "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice"
    - "efficient orthogonal parametrisation of recurrent neural networks using householder reflections"
    - "tunable efficient unitary neural networks eunn and their application to rnns"
    - "a simple way to initialize recurrent networks of rectified linear units"

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)