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: "greedy layerwise learning can scale to imagenet"
  Abstract: Shallow supervised 1-hidden layer neural networks have a number of favorable properties that make them easier to interpret, analyze, and optimize than their deep counterparts, but lack their representational power. Here we use 1-hidden layer learning problems to sequentially build deep networks layer by layer, which can inherit properties from shallow networks. Contrary to previous approaches using shallow networks, we focus on problems where deep learning is reported as critical for success. We thus study CNNs on image classification tasks using the large-scale ImageNet dataset and the CIFAR-10 dataset. Using a simple set of ideas for architecture and training we find that solving sequential 1-hidden-layer auxiliary problems lead to a CNN that exceeds AlexNet performance on ImageNet. Extending this training methodology to construct individual layers by solving 2-and-3-hidden layer auxiliary problems, we obtain an 11-layer network that exceeds several members of the VGG model family on ImageNet, and can train a VGG-11 model to the same accuracy as end-to-end learning. To our knowledge, this is the first competitive alternative to end-to-end training of CNNs that can scale to ImageNet. We illustrate several interesting properties of these models theoretically and conduct a range of experiments to study the properties this training induces on the intermediate layers.

Connected through hyperedge HE-1 (induced by source paper "supervised deep similarity matching"):
  with members:
    - "decoupled greedy learning of cnns"
    - "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"
    - "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-2 (induced by source paper "dbp discrimination based block level pruning for deep model acceleration"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "similarity preserving knowledge distillation"
    - "cop customized deep model compression via regularized correlation based filter level pruning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "knowledge distillation with adversarial samples supporting decision boundary"
    - "adversarial network compression"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-3 (induced by source paper "learning boolean circuits with neural networks"):
  with members:
    - "decoupled greedy learning of cnns"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "effects of depth width and initialization a convergence analysis of layer wise training for deep linear neural networks"):
  with members:
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "gradient descent finds global minima of deep neural networks"
    - "gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks"
    - "a provably correct algorithm for deep learning that actually works"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"
    - "fenchel lifted networks a lagrange relaxation of neural network training"
    - "a convergence analysis of gradient descent for deep linear neural networks"

Connected through hyperedge HE-5 (induced by source paper "putting an end to end to end gradient isolated learning of representations"):
  with members:
    - "data efficient image recognition with contrastive predictive coding"
    - "error forward propagation reusing feedforward connections to propagate errors in deep learning"
    - "biologically plausible learning algorithms can scale to large datasets"
    - "auto encoding total correlation explanation"
    - "i revnet deep invertible networks"
    - "information theoretic co training"
    - "conducting credit assignment by aligning local representations"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-6 (induced by source paper "decoupled greedy learning of cnns"):
  with members:
    - "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"
    - "training neural networks using features replay"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "memorization in overparameterized autoencoders"):
  with members:
    - "how do infinite width bounded norm networks look in function space"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "deja vu an empirical evaluation of the memorization properties of convnets"
    - "deep image prior"
    - "sgd learns over parameterized networks that provably generalize on linearly separable data"
    - "understanding deep learning requires rethinking generalization"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-8 (induced by source paper "shallow deep networks understanding and mitigating network overthinking"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "one bit matters understanding adversarial examples as the abuse of redundancy"
    - "on calibration of modern neural networks"
    - "idk cascades fast deep learning by learning not to overthink"
    - "adaptive neural networks for efficient inference"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

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)