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: "towards non saturating recurrent units for modelling long term dependencies"
  Abstract: Modelling long-term dependencies is a challenge for recurrent neural networks. This is primarily due to the fact that gradients vanish during training, as the sequence length increases. Gradients can be attenuated by transition operators and are attenuated or dropped by activation functions. Canonical architectures like LSTM alleviate this issue by skipping information through a memory mechanism. We propose a new recurrent architecture (Non-saturating Recurrent Unit; NRU) that relies on a memory mechanism but forgoes both saturating activation functions and saturating gates, in order to further alleviate vanishing gradients. In a series of synthetic and real world tasks, we demonstrate that the proposed model is the only model that performs among the top 2 models across all tasks with and without long-term dependencies, when compared against a range of other architectures.

Connected through hyperedge HE-1 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units"):
  with members:
    - "what is wrong with scene text recognition model comparisons dataset and model analysis"
    - "non normal recurrent neural network nnrnn learning long time dependencies while improving expressivity with transient dynamics"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "simple recurrent units for highly parallelizable recurrence"
    - "lstm a search space odyssey"
    - "a flexible approach to automated rnn architecture generation"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"
    - "focusing attention towards accurate text recognition in natural images"

Connected through hyperedge HE-2 (induced by source paper "a spike in performance training hybrid spiking neural networks with quantized activation functions"):
  with members:
    - "recognizing images with at most one spike per neuron"
    - "improving efficiency in neural network accelerator using operands hamming distance optimization"
    - "bit efficient quantization for deep neural networks"
    - "cold case the lost mnist digits"
    - "stochastic rounding and reduced precision fixed point arithmetic for solving neural odes"
    - "spike train level backpropagation for training deep recurrent spiking neural networks"
    - "energy aware neural architecture optimization with fast splitting steepest descent"
    - "nengodl combining deep learning and neuromorphic modelling methods"

Connected through hyperedge HE-3 (induced by source paper "beyond exploding and vanishing gradients analysing rnn training using attractors and smoothness"):
  with members:
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "stable recurrent models"
    - "language modeling with gated convolutional networks"
    - "unitary evolution recurrent neural networks"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-4 (induced by source paper "improving the gating mechanism of recurrent neural networks"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "fast parametric learning with activation memorization"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "towards binary valued gates for robust lstm training"
    - "optimizing agent behavior over long time scales by transporting value"
    - "trellis networks for sequence modeling"
    - "can recurrent neural networks warp time"

Connected through hyperedge HE-5 (induced by source paper "characteristic regularisation for super resolving face images"):
  with members:
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "super identity convolutional neural network for face hallucination"
    - "low resolution face recognition"
    - "surveillance face recognition challenge"
    - "image to image translation for domain adaptation"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "cycada cycle consistent adversarial domain adaptation"
    - "learning to discover cross domain relations with generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "lstm sharp an adaptable energy efficient hardware accelerator for long short term memory"):
  with members:
    - "learning to ask unanswerable questions for machine reading comprehension"
    - "the emergence of number and syntax units in lstm language models"
    - "recent advances in recurrent neural networks"
    - "in datacenter performance analysis of a tensor processing unit"
    - "bidirectional attention flow for machine comprehension"
    - "optimizing performance of recurrent neural networks on gpus"
    - "eie efficient inference engine on compressed deep neural network"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-7 (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"
    - "improved memory in recurrent neural networks with sequential non normal dynamics"
    - "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)