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: "recent advances in recurrent neural networks"
  Abstract: Recurrent neural networks (RNNs) are capable of learning features and long term dependencies from sequential and time-series data. The RNNs have a stack of non-linear units where at least one connection between units forms a directed cycle. A well-trained RNN can model any dynamical system; however, training RNNs is mostly plagued by issues in learning long-term dependencies. In this paper, we present a survey on RNNs and several new advances for newcomers and professionals in the field. The fundamentals and recent advances are explained and the research challenges are introduced.

Connected through hyperedge HE-1 (induced by source paper "fundamentals of recurrent neural network rnn and long short term memory lstm network"):
  with members:
    - "latent odes for irregularly sampled time series"
    - "long short term memory as a dynamically computed element wise weighted sum"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "neural ordinary differential equations"
    - "nais net stable deep networks from non autonomous differential equations"
    - "lstm a search space odyssey"
    - "beyond finite layer neural networks bridging deep architectures and numerical differential equations"

Connected through hyperedge HE-2 (induced by source paper "a survey of optimization methods from a machine learning perspective"):
  with members:
    - "stochastic conjugate gradient algorithm with variance reduction"
    - "compatible natural gradient policy search"
    - "on the convergence of adam and beyond"
    - "normalized direction preserving adam"
    - "task agnostic meta learning for few shot learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "prototypical networks for few shot learning"
    - "decoupled weight decay regularization"

Connected through hyperedge HE-3 (induced by source paper "recurrent neural networks an embedded computing perspective"):
  with members:
    - "deep neural network approximation for custom hardware where we ve been where we re going"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "single stream parallelization of recurrent neural networks for low power and fast inference"
    - "effective quantization approaches for recurrent neural networks"
    - "alternating multi bit quantization for recurrent neural networks"
    - "a survey on methods and theories of quantized neural networks"
    - "finn l library extensions and design trade off analysis for variable precision lstm networks on fpgas"
    - "tensor decomposition for compressing recurrent neural network"

Connected through hyperedge HE-4 (induced by source paper "survey of dropout methods for deep neural networks"):
  with members:
    - "effective and efficient dropout for deep convolutional neural networks"
    - "adversarial dropout for recurrent neural networks"
    - "ising dropout a regularization method for training and compression of deep neural networks"
    - "on the state of the art of evaluation in neural language models"
    - "dropout inference in bayesian neural networks with alpha divergences"
    - "regularizing and optimizing lstm language models"
    - "improved regularization of convolutional neural networks with cutout"
    - "adversarial dropout regularization"

Connected through hyperedge HE-5 (induced by source paper "can sgd learn recurrent neural networks with provable generalization"):
  with members:
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "on the convergence rate of training recurrent neural networks"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"
    - "theoretical properties of the global optimizer of two layer neural network"
    - "a convergence theory for deep learning via over parameterization"
    - "on generalization bounds of a family of recurrent neural networks"
    - "learning one hidden layer neural networks with landscape design"

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"
    - "towards non saturating recurrent units for modelling long term dependencies"
    - "the emergence of number and syntax units in lstm language models"
    - "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 "engn a high throughput and energy efficient accelerator for large graph neural networks"):
  with members:
    - "bandwidth reduction using importance weighted pruning on ring allreduce"
    - "cross lingual knowledge graph alignment via graph matching neural network"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "adaptive sampling towards fast graph representation learning"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "how powerful are graph neural networks"
    - "efficient processing of deep neural networks a tutorial and survey"

Connected through hyperedge HE-8 (induced by source paper "robust online model adaptation by extended kalman filter with exponential moving average and dynamic multi epoch strategy"):
  with members:
    - "human motion trajectory prediction a survey"
    - "adaptable human intention and trajectory prediction for human robot collaboration"
    - "human motion prediction using semi adaptable neural networks"
    - "multi agent imitation learning for driving simulation"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

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)