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: "minimal gated unit for recurrent neural networks"
  Abstract: Recently recurrent neural networks (RNN) has been very successful in handling sequence data. However, understanding RNN and finding the best practices for RNN is a difficult task, partly because there are many competing and complex hidden units (such as LSTM and GRU). We propose a gated unit for RNN, named as Minimal Gated Unit (MGU), since it only contains one gate, which is a minimal design among all gated hidden units. The design of MGU benefits from evaluation results on LSTM and GRU in the literature. Experiments on various sequence data show that MGU has comparable accuracy with GRU, but has a simpler structure, fewer parameters, and faster training. Hence, MGU is suitable in RNN's applications. Its simple architecture also means that it is easier to evaluate and tune, and in principle it is easier to study MGU's properties theoretically and empirically.

Connected through hyperedge HE-1 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units"):
  with members:
    - "towards non saturating recurrent units for modelling long term dependencies"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "a bayesian approach to recurrence in neural networks"):
  with members:
    - "improving speech recognition by revising gated recurrent units"
    - "the microsoft 2016 conversational speech recognition system"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-3 (induced by source paper "deep independently recurrent neural network indrnn"):
  with members:
    - "temporal transformer networks joint learning of invariant and discriminative time warping"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "part based graph convolutional network for action recognition"
    - "learning long term dependencies via fourier recurrent units"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "view adaptive neural networks for high performance skeleton based human action recognition"
    - "simple recurrent units for highly parallelizable recurrence"
    - "lstm a search space odyssey"

Connected through hyperedge HE-4 (induced by source paper "an optimized recurrent unit for ultra low power keyword spotting"):
  with members:
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "cmsis nn efficient neural network kernels for arm cortex m cpus"
    - "improving speech recognition by revising gated recurrent units"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "deep neural networks are robust to weight binarization and other non linear distortions"
    - "recurrent neural networks with limited numerical precision"
    - "quantized neural networks training neural networks with low precision weights and activations"

Connected through hyperedge HE-5 (induced by source paper "gru ode bayes continuous modeling of sporadically observed time series"):
  with members:
    - "early recognition of sepsis with gaussian process temporal convolutional networks and dynamic time warping"
    - "latent odes for irregularly sampled time series"
    - "deep ensemble tensor factorization for longitudinal patient trajectories classification"
    - "stable architectures for deep neural networks"
    - "neural ordinary differential equations"
    - "scalable and accurate deep learning for electronic health records"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "directly modeling missing data in sequences with rnns improved classification of clinical time series"

Connected through hyperedge HE-6 (induced by source paper "an empirical exploration of deep recurrent connections and memory cells using neuro evolution"):
  with members:
    - "a specialized evolutionary strategy using mean absolute error random sampling to design recurrent neural networks"
    - "investigating recurrent neural network memory structures using neuro evolution"
    - "low cost recurrent neural network expected performance evaluation"
    - "from nodes to networks evolving recurrent neural networks"
    - "evolving deep neural networks"
    - "gated orthogonal recurrent units on learning to forget"
    - "learning simpler language models with the differential state framework"
    - "evolving deep convolutional neural networks for image classification"

Connected through hyperedge HE-7 (induced by source paper "investigating recurrent neural network memory structures using neuro evolution"):
  with members:
    - "accelerating the evolution of convolutional neural networks with node level mutations and epigenetic weight initialization"
    - "from nodes to networks evolving recurrent neural networks"
    - "perturbative neural networks"
    - "evolving deep neural networks"
    - "large scale evolution of image classifiers"
    - "learning simpler language models with the differential state framework"
    - "evolving deep convolutional neural networks for image classification"
    - "a genetic programming approach to designing convolutional neural network architectures"

Connected through hyperedge HE-8 (induced by source paper "learning longer term dependencies via grouped distributor unit"):
  with members:
    - "lstm a search space odyssey"
    - "analyzing and exploiting narx recurrent neural networks for long term dependencies"
    - "dilated recurrent neural networks"
    - "recurrent batch normalization"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "unitary evolution recurrent neural networks"
    - "adam a method for stochastic optimization"

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)