human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "adaptively truncating backpropagation through time to control gradient bias"):
  Abstract: Truncated backpropagation through time (TBPTT) is a popular method for learning in recurrent neural networks (RNNs) that saves computation and memory at the cost of bias by truncating backpropagation after a fixed number of lags. In practice, choosing the optimal truncation length is difficult: TBPTT will not converge if the truncation length is too small, or will converge slowly if it is too large. We propose an adaptive TBPTT scheme that converts the problem from choosing a temporal lag to one of choosing a tolerable amount of gradient bias. For many realistic RNNs, the TBPTT gradients decay geometrically in expectation for large lags; under this condition, we can control the bias by varying the truncation length adaptively. For RNNs with smooth activation functions, we prove that this bias controls the convergence rate of SGD with biased gradients for our non-convex loss. Using this theory, we develop a practical method for adaptively estimating the truncation length during training. We evaluate our adaptive TBPTT method on synthetic data and language modeling tasks and find that our adaptive TBPTT ameliorates the computational pitfalls of fixed TBPTT.

Includes members:
  Member "approximating real time recurrent learning with random kronecker factors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "equilibrated recurrent neural network neuronal time delayed self feedback improves accuracy and stability")
  Member "stochastic gradient descent with biased but consistent gradient estimators":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "differentiable visual computing")
  Member "unbiasing truncated backpropagation through time":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal kronecker sum approximation of real time recurrent learning")
  Member "stable recurrent models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "non asymptotic and accurate learning of nonlinear dynamical systems")
  Member "on orthogonality and learning recurrent networks with long term dependencies":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "concept whitening for interpretable image recognition")
  Member "quasi recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "pointer sentinel mixture models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "a recurrent neural network without chaos":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "provable benefit of orthogonal initialization in optimizing deep linear networks")

Question: Please tell me which class the source paper 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)