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: "the statistical recurrent unit"
  Abstract: Sophisticated gated recurrent neural network architectures like LSTMs and GRUs have been shown to be highly effective in a myriad of applications. We develop an un-gated unit, the statistical recurrent unit (SRU), that is able to learn long term dependencies in data by only keeping moving averages of statistics. The SRU's architecture is simple, un-gated, and contains a comparable number of parameters to LSTMs; yet, SRUs perform favorably to more sophisticated LSTM and GRU alternatives, often outperforming one or both in various tasks. We show the efficacy of SRUs as compared to LSTMs and GRUs in an unbiased manner by optimizing respective architectures' hyperparameters in a Bayesian optimization scheme for both synthetic and real-world tasks.

Connected through hyperedge HE-1 (induced by source paper "towards non saturating recurrent units for modelling long term dependencies"):
  with members:
    - "can recurrent neural networks warp time"
    - "gated orthogonal recurrent units on learning to forget"
    - "rotational unit of memory"
    - "regularizing and optimizing lstm language models"
    - "memory augmented neural networks with wormhole connections"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "noisy activation functions"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "recurrent attentive neural process for sequential data"):
  with members:
    - "sequential neural processes"
    - "attentive neural processes"
    - "the functional neural process"
    - "neural processes"
    - "consistent generative query networks"
    - "conditional neural processes"
    - "structural recurrent neural network srnn for group activity analysis"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "dilated convolutional neural networks for sequential manifold valued data"):
  with members:
    - "a statistical recurrent model on the manifold of symmetric positive definite matrices"
    - "manifoldnet a deep network framework for manifold valued data"
    - "h cnns convolutional neural networks for riemannian homogeneous spaces"
    - "covariant compositional networks for learning graphs"
    - "tensor train recurrent neural networks for video classification"
    - "geometric deep learning going beyond euclidean data"
    - "second order convolutional neural networks"
    - "building deep networks on grassmann manifolds"

Connected through hyperedge HE-4 (induced by source paper "streaming adaptation of deep forecasting models using adaptive recurrent units"):
  with members:
    - "fast parametric learning with activation memorization"
    - "armdn associative and recurrent mixture density networks for eretail demand forecasting"
    - "learning long term dependencies via fourier recurrent units"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"
    - "labeled memory networks for online model adaptation"
    - "r2n2 residual recurrent neural networks for multivariate time series forecasting"
    - "a simple neural attentive meta learner"

Connected through hyperedge HE-5 (induced by source paper "economy statistical recurrent units for inferring nonlinear granger causality"):
  with members:
    - "a convergence theory for deep learning via over parameterization"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-6 (induced by source paper "on the convergence rate of training recurrent neural networks"):
  with members:
    - "can sgd learn recurrent neural networks with provable generalization"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "non asymptotic identification of lti systems from a single trajectory"
    - "robust spectral filtering and anomaly detection"
    - "on the connection between learning two layers neural networks and tensor decomposition"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "learning long term dependencies via fourier recurrent units"

Connected through hyperedge HE-7 (induced by source paper "low pass recurrent neural networks a memory architecture for longer term correlation discovery"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "unbiased online recurrent optimization"
    - "understanding synthetic gradients and decoupled neural interfaces"
    - "neural episodic control"
    - "scale invariant temporal history sith optimal slicing of the past in an uncertain world"
    - "population based training of neural networks"
    - "associative long short term memory"
    - "reinforcement learning with unsupervised auxiliary tasks"

Connected through hyperedge HE-8 (induced by source paper "an empirical study of language cnn for image captioning"):
  with members:
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "image captioning with semantic attention"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "attention correctness in neural image captioning"
    - "identity mappings in deep residual networks"
    - "spice semantic propositional image caption evaluation"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "recurrent highway 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.LG(Machine Learning)