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: "non normal recurrent neural network nnrnn learning long time dependencies while improving expressivity with transient dynamics"
  Abstract: A recent strategy to circumvent the exploding and vanishing gradient problem in RNNs, and to allow the stable propagation of signals over long time scales, is to constrain recurrent connectivity matrices to be orthogonal or unitary. This ensures eigenvalues with unit norm and thus stable dynamics and training. However this comes at the cost of reduced expressivity due to the limited variety of orthogonal transformations. We propose a novel connectivity structure based on the Schur decomposition and a splitting of the Schur form into normal and non-normal parts. This allows to parametrize matrices with unit-norm eigenspectra without orthogonality constraints on eigenbases. The resulting architecture ensures access to a larger space of spectrally constrained matrices, of which orthogonal matrices are a subset. This crucial difference retains the stability advantages and training speed of orthogonal RNNs while enhancing expressivity, especially on tasks that require computations over ongoing input sequences.

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"
    - "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 "eigenvalue normalized recurrent neural networks for short term memory"):
  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"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"
    - "complex gated recurrent neural networks"
    - "gated orthogonal recurrent units on learning to forget"
    - "fast slow recurrent neural networks"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "efficient orthogonal parametrisation of recurrent neural networks using householder reflections"

Connected through hyperedge HE-3 (induced by source paper "inferring dynamical systems with long range dependencies through line attractor regularization"):
  with members:
    - "learning dynamical systems from partial observations"
    - "gated recurrent units viewed through the lens of continuous time dynamical systems"
    - "identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fmri"
    - "state space lstm models with particle mcmc inference"
    - "gated orthogonal recurrent units on learning to forget"
    - "recurrent orthogonal networks and long memory tasks"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "unitary evolution recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "improved memory in recurrent neural networks with sequential non normal dynamics"):
  with members:
    - "an analysis of neural language modeling at multiple scales"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "recurrent world models facilitate policy evolution"
    - "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice"
    - "dilated recurrent neural networks"
    - "regularizing and optimizing lstm language models"
    - "recurrent orthogonal networks and long memory tasks"
    - "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.LG(Machine Learning)