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: "training neural networks using features replay"
  Abstract: Training a neural network using backpropagation algorithm requires passing error gradients sequentially through the network. The backward locking prevents us from updating network layers in parallel and fully leveraging the computing resources. Recently, there are several works trying to decouple and parallelize the backpropagation algorithm. However, all of them suffer from severe accuracy loss or memory explosion when the neural network is deep. To address these challenging issues, we propose a novel parallel-objective formulation for the objective function of the neural network. After that, we introduce features replay algorithm and prove that it is guaranteed to converge to critical points for the non-convex problem under certain conditions. Finally, we apply our method to training deep convolutional neural networks, and the experimental results show that the proposed method achieves {faster} convergence, {lower} memory consumption, and {better} generalization error than compared methods.

Connected through hyperedge HE-1 (induced by source paper "sideways depth parallel training of video models"):
  with members:
    - "backprop diffusion is biologically plausible"
    - "decoupled greedy learning of cnns"
    - "dynamonet dynamic action and motion network"
    - "putting an end to end to end gradient isolated learning of representations"
    - "scsampler sampling salient clips from video for efficient action recognition"
    - "rethinking imagenet pre training"
    - "learning visual question answering by bootstrapping hard attention"
    - "massively parallel video networks"

Connected through hyperedge HE-2 (induced by source paper "inner imaging networks put lenses into convolutional structure"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "dropblock a regularization method for convolutional networks"
    - "convolutional neural networks with alternately updated clique"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "channelnets compact and efficient convolutional neural networks via channel wise convolutions"
    - "gather excite exploiting feature context in convolutional neural networks"
    - "sanity checks for saliency maps"

Connected through hyperedge HE-3 (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"
    - "decoupled parallel backpropagation with convergence guarantee"
    - "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-4 (induced by source paper "improving discrete latent representations with differentiable approximation bridges"):
  with members:
    - "decoupled greedy learning of cnns"
    - "a new benchmark and progress toward improved weakly supervised learning"
    - "deep learning using rectified linear units relu"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "neural arithmetic logic units"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "self normalizing neural networks"

Connected through hyperedge HE-5 (induced by source paper "diversely stale parameters for efficient training of cnns"):
  with members:
    - "decoupled greedy learning of cnns"
    - "ouroboros on accelerating training of transformer based language models"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "rethinking the value of network pruning"
    - "squeeze and excitation networks"
    - "aggregated residual transformations for deep neural networks"
    - "decoupled neural interfaces using synthetic gradients"
    - "training deep nets with sublinear memory cost"

Connected through hyperedge HE-6 (induced by source paper "fully decoupled neural network learning using delayed gradients"):
  with members:
    - "decoupled greedy learning of cnns"
    - "beyond data and model parallelism for deep neural networks"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "deep supervised learning using local errors"
    - "revisiting distributed synchronous sgd"
    - "shake shake regularization"

Connected through hyperedge HE-7 (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"
    - "decoupled parallel backpropagation with convergence guarantee"
    - "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-8 (induced by source paper "decoupled greedy learning of cnns"):
  with members:
    - "greedy layerwise learning can scale to imagenet"
    - "biologically plausible learning algorithms can scale to large datasets"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "latent space policies for hierarchical reinforcement learning"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "understanding synthetic gradients and decoupled neural interfaces"
    - "learning deep resnet blocks sequentially using boosting theory"
    - "accurate large minibatch sgd training imagenet in 1 hour"

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)