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: "kronecker recurrent units"
  Abstract: Our work addresses two important issues with recurrent neural networks: (1) they are over-parameterized, and (2) the recurrence matrix is ill-conditioned. The former increases the sample complexity of learning and the training time. The latter causes the vanishing and exploding gradient problem. We present a flexible recurrent neural network model called Kronecker Recurrent Units (KRU). KRU achieves parameter efficiency in RNNs through a Kronecker factored recurrent matrix. It overcomes the ill-conditioning of the recurrent matrix by enforcing soft unitary constraints on the factors. Thanks to the small dimensionality of the factors, maintaining these constraints is computationally efficient. Our experimental results on seven standard data-sets reveal that KRU can reduce the number of parameters by three orders of magnitude in the recurrent weight matrix compared to the existing recurrent models, without trading the statistical performance. These results in particular show that while there are advantages in having a high dimensional recurrent space, the capacity of the recurrent part of the model can be dramatically reduced.

Connected through hyperedge HE-1 (induced by source paper "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"):
  with members:
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"
    - "on the state of the art of evaluation in neural language models"
    - "gated orthogonal recurrent units on learning to forget"
    - "regularizing and optimizing lstm language models"
    - "size independent sample complexity of neural networks"
    - "exploring sparsity in recurrent neural networks"
    - "skip rnn learning to skip state updates in recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "implementation of optical deep neural networks using the fabry perot interferometer"):
  with members:
    - "large scale optical neural networks based on photoelectric multiplication"
    - "reinforcement learning in a large scale photonic recurrent neural network"
    - "gated orthogonal recurrent units on learning to forget"
    - "the numerics of gans"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "searching for activation functions"
    - "associative long short term memory"
    - "on complex valued convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "dynamical isometry and a mean field theory of lstms and grus"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "a mean field theory of batch normalization"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "a convergence theory for deep learning via over parameterization"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice"
    - "mean field residual networks on the edge of chaos"

Connected through hyperedge HE-4 (induced by source paper "stable recurrent models"):
  with members:
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "stochastic gradient descent learns state equations with nonlinear activations"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"
    - "convolutional sequence to sequence learning"
    - "on orthogonality and learning recurrent networks with long term dependencies"

Connected through hyperedge HE-5 (induced by source paper "igloo slicing the features space to represent long sequences"):
  with members:
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "simple recurrent units for highly parallelizable recurrence"
    - "deep learning with convolutional neural networks for decoding and visualization of eeg pathology"
    - "recurrent additive networks"
    - "dilated recurrent neural networks"
    - "quasi recurrent neural networks"
    - "wavenet a generative model for raw audio"

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)