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: "approximating real time recurrent learning with random kronecker factors"
  Abstract: Despite all the impressive advances of recurrent neural networks, sequential data is still in need of better modelling. Truncated backpropagation through time (TBPTT), the learning algorithm most widely used in practice, suffers from the truncation bias, which drastically limits its ability to learn long-term dependencies.The Real-Time Recurrent Learning algorithm (RTRL) addresses this issue, but its high computational requirements make it infeasible in practice. The Unbiased Online Recurrent Optimization algorithm (UORO) approximates RTRL with a smaller runtime and memory cost, but with the disadvantage of obtaining noisy gradients that also limit its practical applicability. In this paper we propose the Kronecker Factored RTRL (KF-RTRL) algorithm that uses a Kronecker product decomposition to approximate the gradients for a large class of RNNs. We show that KF-RTRL is an unbiased and memory efficient online learning algorithm. Our theoretical analysis shows that, under reasonable assumptions, the noise introduced by our algorithm is not only stable over time but also asymptotically much smaller than the one of the UORO algorithm. We also confirm these theoretical results experimentally. Further, we show empirically that the KF-RTRL algorithm captures long-term dependencies and almost matches the performance of TBPTT on real world tasks by training Recurrent Highway Networks on a synthetic string memorization task and on the Penn TreeBank task, respectively. These results indicate that RTRL based approaches might be a promising future alternative to TBPTT.

Connected through hyperedge HE-1 (induced by source paper "equilibrated recurrent neural network neuronal time delayed self feedback improves accuracy and stability"):
  with members:
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "reviving and improving recurrent back propagation"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "reversible recurrent neural networks"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"
    - "simple recurrent units for highly parallelizable recurrence"
    - "attention is all you need"
    - "unbiased online recurrent optimization"

Connected through hyperedge HE-2 (induced by source paper "optimal kronecker sum approximation of real time recurrent learning"):
  with members:
    - "on the variance of unbiased online recurrent optimization"
    - "unbiasing truncated backpropagation through time"
    - "sparse attentive backtracking temporal creditassignment through reminding"
    - "reviving and improving recurrent back propagation"
    - "an analysis of neural language modeling at multiple scales"
    - "on the state of the art of evaluation in neural language models"
    - "attention is all you need"
    - "unbiased online recurrent optimization"

Connected through hyperedge HE-3 (induced by source paper "decoupling hierarchical recurrent neural networks with locally computable losses"):
  with members:
    - "optimal kronecker sum approximation of real time recurrent learning"
    - "on the variance of unbiased online recurrent optimization"
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "unbiased online recurrent optimization"
    - "asynchronous methods for deep reinforcement learning"
    - "decoupled neural interfaces using synthetic gradients"
    - "training deep nets with sublinear memory cost"
    - "hierarchical multiscale recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "adaptively truncating backpropagation through time to control gradient bias"):
  with members:
    - "stochastic gradient descent with biased but consistent gradient estimators"
    - "unbiasing truncated backpropagation through time"
    - "stable recurrent models"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "quasi recurrent neural networks"
    - "pointer sentinel mixture models"
    - "a recurrent neural network without chaos"
    - "unitary evolution recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "on the variance of unbiased online recurrent optimization"):
  with members:
    - "optimizing agent behavior over long time scales by transporting value"
    - "reversible recurrent neural networks"
    - "rudder return decomposition for delayed rewards"
    - "the reversible residual network backpropagation without storing activations"
    - "unbiased online recurrent optimization"
    - "training deep nets with sublinear memory cost"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "training recurrent networks online without backtracking"

Connected through hyperedge HE-6 (induced by source paper "a unified framework of online learning algorithms for training recurrent neural networks"):
  with members:
    - "optimal kronecker sum approximation of real time recurrent learning"
    - "on the variance of unbiased online recurrent optimization"
    - "continual learning of recurrent neural networks by locally aligning distributed representations"
    - "learning to adapt by minimizing discrepancy"
    - "unbiased online recurrent optimization"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-7 (induced by source paper "continual learning of recurrent neural networks by locally aligning distributed representations"):
  with members:
    - "a neural temporal model for human motion prediction"
    - "sparse attentive backtracking long range credit assignment in recurrent networks"
    - "biologically motivated algorithms for propagating local target representations"
    - "conducting credit assignment by aligning local representations"
    - "lstm a search space odyssey"
    - "learning to adapt by minimizing discrepancy"
    - "learning simpler language models with the differential state framework"
    - "unbiased online recurrent optimization"

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)