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: "decoupled parallel backpropagation with convergence guarantee"
  Abstract: Backpropagation algorithm is indispensable for the training of feedforward neural networks. It requires propagating error gradients sequentially from the output layer all the way back to the input layer. The backward locking in backpropagation algorithm constrains us from updating network layers in parallel and fully leveraging the computing resources. Recently, several algorithms have been proposed for breaking the backward locking. However, their performances degrade seriously when networks are deep. In this paper, we propose decoupled parallel backpropagation algorithm for deep learning optimization with convergence guarantee. Firstly, we decouple the backpropagation algorithm using delayed gradients, and show that the backward locking is removed when we split the networks into multiple modules. Then, we utilize decoupled parallel backpropagation in two stochastic methods and prove that our method guarantees convergence to critical points for the non-convex problem. Finally, we perform experiments for training deep convolutional neural networks on benchmark datasets. The experimental results not only confirm our theoretical analysis, but also demonstrate that the proposed method can achieve significant speedup without loss of accuracy.

Connected through hyperedge HE-1 (induced by source paper "ouroboros on accelerating training of transformer based language models"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "training neural networks using features replay"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "character level language modeling with deeper self attention"
    - "attention is all you need"
    - "decoupled weight decay regularization"
    - "a deep generative framework for paraphrase generation"
    - "paraphrase generation with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "xpipe efficient pipeline model parallelism for multi gpu dnn training"):
  with members:
    - "deep audio visual speech recognition"
    - "optimizing multi gpu parallelization strategies for deep learning training"
    - "ampnet asynchronous model parallel training for dynamic neural networks"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "training neural networks using features replay"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "efficient and robust parallel dnn training through model parallelism on multi gpu platform"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"

Connected through hyperedge HE-3 (induced by source paper "associated learning decomposing end to end backpropagation based on auto encoders and target propagation"):
  with members:
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "training neural networks using features replay"
    - "biologically motivated algorithms for propagating local target representations"
    - "conducting credit assignment by aligning local representations"
    - "deep supervised learning using local errors"
    - "dynamic routing between capsules"
    - "decoupled neural interfaces using synthetic gradients"
    - "training neural networks without gradients a scalable admm approach"

Connected through hyperedge HE-4 (induced by source paper "training neural networks using features replay"):
  with members:
    - "revisiting distributed synchronous sgd"
    - "benefits of depth in neural networks"
    - "densely connected convolutional networks"
    - "asynchronous methods for deep reinforcement learning"
    - "decoupled neural interfaces using synthetic gradients"
    - "training neural networks without gradients a scalable admm approach"
    - "the power of depth for feedforward neural networks"
    - "continuous control with deep reinforcement learning"

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)