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 "decoupling hierarchical recurrent neural networks with locally computable losses"):
  Abstract: Learning long-term dependencies is a key long-standing challenge of recurrent neural networks (RNNs). Hierarchical recurrent neural networks (HRNNs) have been considered a promising approach as long-term dependencies are resolved through shortcuts up and down the hierarchy. Yet, the memory requirements of Truncated Backpropagation Through Time (TBPTT) still prevent training them on very long sequences. In this paper, we empirically show that in (deep) HRNNs, propagating gradients back from higher to lower levels can be replaced by locally computable losses, without harming the learning capability of the network, over a wide range of tasks. This decoupling by local losses reduces the memory requirements of training by a factor exponential in the depth of the hierarchy in comparison to standard TBPTT.

Includes members:
  Member "optimal kronecker sum approximation of real time recurrent learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a unified framework of online learning algorithms for training recurrent neural networks")
  Member "on the variance of unbiased online recurrent optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal kronecker sum approximation of real time recurrent learning")
  Member "approximating real time recurrent learning with random kronecker factors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "equilibrated recurrent neural network neuronal time delayed self feedback improves accuracy and stability")
  Member "differentiable plasticity training plastic neural networks with backpropagation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neuroevolution of self interpretable agents")
  Member "unbiased online recurrent optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "equilibrated recurrent neural network neuronal time delayed self feedback improves accuracy and stability")
  Member "asynchronous methods for deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "decoupled neural interfaces using synthetic gradients":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sideways depth parallel training of video models")
  Member "training deep nets with sublinear memory cost":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "searching for winograd aware quantized 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)