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 greedy learning of cnns"
  Abstract: A commonly cited inefficiency of neural network training by back-propagation is the update locking problem: each layer must wait for the signal to propagate through the network before updating. We consider and analyze a training procedure, Decoupled Greedy Learning (DGL), that addresses this problem more effectively and at scales beyond those of previous solutions. It is based on a greedy relaxation of the joint training objective, recently shown to be effective in the context of Convolutional Neural Networks (CNNs) on large-scale image classification. We consider an optimization of this objective that permits us to decouple the layer training, allowing for layers or modules in networks to be trained with a potentially linear parallelization in layers. We show theoretically and empirically that this approach converges. In addition, we empirically find that it can lead to better generalization than sequential greedy optimization and even standard end-to-end back-propagation. We show that an extension of this approach to asynchronous settings, where modules can operate with large communication delays, is possible with the use of a replay buffer. We demonstrate the effectiveness of DGL on the CIFAR-10 datasets against alternatives and on the large-scale ImageNet dataset, where we are able to effectively train VGG and ResNet-152 models.

Connected through hyperedge HE-1 (induced by source paper "sideways depth parallel training of video models"):
  with members:
    - "backprop diffusion is biologically plausible"
    - "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"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-2 (induced by source paper "supervised deep similarity matching"):
  with members:
    - "structured and deep similarity matching via structured and deep hebbian networks"
    - "learning from brains how to regularize machines"
    - "a neural network for semi supervised learning on manifolds"
    - "greedy layerwise learning can scale to imagenet"
    - "biologically motivated algorithms for propagating local target representations"
    - "conducting credit assignment by aligning local representations"
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"
    - "theano a python framework for fast computation of mathematical expressions"

Connected through hyperedge HE-3 (induced by source paper "online learned continual compression with stacked quantization modules"):
  with members:
    - "generating diverse high fidelity images with vq vae 2"
    - "unsupervised state representation learning in atari"
    - "continual learning with tiny episodic memories"
    - "compressing the input for cnns with the first order scattering transform"
    - "experience replay for continual learning"
    - "efficient lifelong learning with a gem"
    - "deep generative modeling of lidar data"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"

Connected through hyperedge HE-4 (induced by source paper "learning boolean circuits with neural networks"):
  with members:
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "id3 learns juntas for smoothed product distributions"
    - "on the learnability of deep random networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "on the power and limitations of random features for understanding neural networks"
    - "a comparative analysis of the optimization and generalization property of two layer neural network and random feature models under gradient descent dynamics"
    - "what can resnet learn efficiently going beyond kernels"
    - "is deeper better only when shallow is good"

Connected through hyperedge HE-5 (induced by source paper "improving discrete latent representations with differentiable approximation bridges"):
  with members:
    - "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"
    - "training neural networks using features replay"
    - "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-6 (induced by source paper "diversely stale parameters for efficient training of cnns"):
  with members:
    - "ouroboros on accelerating training of transformer based language models"
    - "training neural networks using features replay"
    - "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-7 (induced by source paper "fully decoupled neural network learning using delayed gradients"):
  with members:
    - "beyond data and model parallelism for deep neural networks"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "training neural networks using features replay"
    - "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"

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)