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: "on the variance of unbiased online recurrent optimization"
  Abstract: The recently proposed Unbiased Online Recurrent Optimization algorithm (UORO, arXiv:1702.05043) uses an unbiased approximation of RTRL to achieve fully online gradient-based learning in RNNs. In this work we analyze the variance of the gradient estimate computed by UORO, and propose several possible changes to the method which reduce this variance both in theory and practice. We also contribute significantly to the theoretical and intuitive understanding of UORO (and its existing variance reduction technique), and demonstrate a fundamental connection between its gradient estimate and the one that would be computed by REINFORCE if small amounts of noise were added to the RNN's hidden units.

Connected through hyperedge HE-1 (induced by source paper "optimal kronecker sum approximation of real time recurrent learning"):
  with members:
    - "approximating real time recurrent learning with random kronecker factors"
    - "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-2 (induced by source paper "decoupling hierarchical recurrent neural networks with locally computable losses"):
  with members:
    - "optimal kronecker sum approximation of real time recurrent learning"
    - "approximating real time recurrent learning with random kronecker factors"
    - "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-3 (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"
    - "approximating real time recurrent learning with random kronecker factors"
    - "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"

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)