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: "gated orthogonal recurrent units on learning to forget"
  Abstract: We present a novel recurrent neural network (RNN) based model that combines the remembering ability of unitary RNNs with the ability of gated RNNs to effectively forget redundant/irrelevant information in its memory. We achieve this by extending unitary RNNs with a gating mechanism. Our model is able to outperform LSTMs, GRUs and Unitary RNNs on several long-term dependency benchmark tasks. We empirically both show the orthogonal/unitary RNNs lack the ability to forget and also the ability of GORU to simultaneously remember long term dependencies while forgetting irrelevant information. This plays an important role in recurrent neural networks. We provide competitive results along with an analysis of our model on many natural sequential tasks including the bAbI Question Answering, TIMIT speech spectrum prediction, Penn TreeBank, and synthetic tasks that involve long-term dependencies such as algorithmic, parenthesis, denoising and copying tasks.

Connected through hyperedge HE-1 (induced by source paper "generating high fidelity synthetic time series datasets with doppelganger"):
  with members:
    - "infogan cr disentangling generative adversarial networks with contrastive regularizers"
    - "differentially private generative adversarial networks for time series continuous and discrete open data"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "a statistical recurrent model on the manifold of symmetric positive definite matrices"
    - "differentially private generative adversarial network"
    - "progressive growing of gans for improved quality stability and variation"
    - "generating multi label discrete patient records using generative adversarial networks"

Connected through hyperedge HE-2 (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"
    - "kronecker recurrent units"
    - "on the state of the art of evaluation in neural language models"
    - "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-3 (induced by source paper "towards non saturating recurrent units for modelling long term dependencies"):
  with members:
    - "can recurrent neural networks warp time"
    - "the statistical recurrent unit"
    - "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-4 (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"
    - "non normal recurrent neural network nnrnn learning long time dependencies while improving expressivity with transient dynamics"
    - "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"
    - "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-5 (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"
    - "kronecker recurrent units"
    - "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-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"
    - "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-7 (induced by source paper "input output equivalence of unitary and contractive rnns"):
  with members:
    - "exploring generalization in deep learning"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "efficient orthogonal parametrisation of recurrent neural networks using householder reflections"
    - "tunable efficient unitary neural networks eunn and their application to rnns"
    - "unitary evolution recurrent neural networks"
    - "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"

Connected through hyperedge HE-8 (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"
    - "non normal recurrent neural network nnrnn learning long time dependencies while improving expressivity with transient dynamics"
    - "state space lstm models with particle mcmc inference"
    - "recurrent orthogonal networks and long memory tasks"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "unitary evolution recurrent 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.LG(Machine Learning)