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: "assessing the scalability of biologically motivated deep learning algorithms and architectures"
  Abstract: The backpropagation of error algorithm (BP) is impossible to implement in a real brain. The recent success of deep networks in machine learning and AI, however, has inspired proposals for understanding how the brain might learn across multiple layers, and hence how it might approximate BP. As of yet, none of these proposals have been rigorously evaluated on tasks where BP-guided deep learning has proved critical, or in architectures more structured than simple fully-connected networks. Here we present results on scaling up biologically motivated models of deep learning on datasets which need deep networks with appropriate architectures to achieve good performance. We present results on the MNIST, CIFAR-10, and ImageNet datasets and explore variants of target-propagation (TP) and feedback alignment (FA) algorithms, and explore performance in both fully- and locally-connected architectures. We also introduce weight-transport-free variants of difference target propagation (DTP) modified to remove backpropagation from the penultimate layer. Many of these algorithms perform well for MNIST, but for CIFAR and ImageNet we find that TP and FA variants perform significantly worse than BP, especially for networks composed of locally connected units, opening questions about whether new architectures and algorithms are required to scale these approaches. Our results and implementation details help establish baselines for biologically motivated deep learning schemes going forward.

Connected through hyperedge HE-1 (induced by source paper "revisiting spatial invariance with low rank local connectivity"):
  with members:
    - "condconv conditionally parameterized convolutions for efficient inference"
    - "stand alone self attention in vision models"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "attention branch network learning of attention mechanism for visual explanation"
    - "learn to pay attention"
    - "cbam convolutional block attention module"
    - "you look twice gaternet for dynamic filter selection in cnns"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-2 (induced by source paper "biologically motivated deep learning method using hierarchical competitive learning"):
  with members:
    - "unsupervised learning by competing hidden units"
    - "competitive learning enriches learning representation and accelerates the fine tuning of cnns"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "how auto encoders could provide credit assignment in deep networks via target propagation"
    - "going deeper with convolutions"
    - "difference target propagation"
    - "winner take all autoencoders"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-3 (induced by source paper "are skip connections necessary for biologically plausible learning rules"):
  with members:
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "difference target propagation"

Connected through hyperedge HE-4 (induced by source paper "stand alone self attention in vision models"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "an empirical study of spatial attention mechanisms in deep networks"
    - "local relation networks for image recognition"
    - "nas fpn learning scalable feature pyramid architecture for object detection"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "self attention with relative position representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "accelerated cnn training through gradient approximation"):
  with members:
    - "training simplification and model simplification for deep learning a minimal effort back propagation method"
    - "prunetrain gradual structured pruning from scratch for faster neural network training"
    - "prunetrain fast neural network training by dynamic sparse model reconfiguration"
    - "biologically plausible learning algorithms can scale to large datasets"
    - "nvidia tensor core programmability performance precision"
    - "faster neural network training with approximate tensor operations"
    - "sbnet sparse blocks network for fast inference"
    - "dropback continuous pruning during training"

Connected through hyperedge HE-6 (induced by source paper "biologically plausible deep learning but how far can we go with shallow networks"):
  with members:
    - "unsupervised learning by competing hidden units"
    - "deep learning in spiking neural networks"
    - "putting a bug in ml the moth olfactory network learns to read mnist"
    - "error forward propagation reusing feedforward connections to propagate errors in deep learning"
    - "feedback alignment in deep convolutional networks"
    - "regularized evolution for image classifier architecture search"
    - "a biologically plausible learning rule for deep learning in the brain"
    - "direct training for spiking neural networks faster larger better"

Connected through hyperedge HE-7 (induced by source paper "the hsic bottleneck deep learning without back propagation"):
  with members:
    - "on mutual information maximization for representation learning"
    - "error forward propagation reusing feedforward connections to propagate errors in deep learning"
    - "biologically plausible learning algorithms can scale to large datasets"
    - "feedback alignment in deep convolutional networks"
    - "a biologically plausible learning rule for deep learning in the brain"
    - "intriguing properties of randomly weighted networks generalizing while learning next to nothing"
    - "gradient target propagation"
    - "dependency bottleneck in auto encoding architectures an empirical study"

Connected through hyperedge HE-8 (induced by source paper "spikegrad an ann equivalent computation model for implementing backpropagation with spikes"):
  with members:
    - "deep spiking neural network with spike count based learning rule"
    - "a spiking network for inference of relations trained with neuromorphic backpropagation"
    - "deep learning in spiking neural networks"
    - "training deep neural networks for binary communication with the whetstone method"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "direct training for spiking neural networks faster larger better"
    - "long short term memory and learning to learn in networks of spiking neurons"

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)