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: "learning deep resnet blocks sequentially using boosting theory"
  Abstract: Deep neural networks are known to be difficult to train due to the instability of back-propagation. A deep \emph{residual network} (ResNet) with identity loops remedies this by stabilizing gradient computations. We prove a boosting theory for the ResNet architecture. We construct $T$ weak module classifiers, each contains two of the $T$ layers, such that the combined strong learner is a ResNet. Therefore, we introduce an alternative Deep ResNet training algorithm, \emph{BoostResNet}, which is particularly suitable in non-differentiable architectures. Our proposed algorithm merely requires a sequential training of $T$ "shallow ResNets" which are inexpensive. We prove that the training error decays exponentially with the depth $T$ if the \emph{weak module classifiers} that we train perform slightly better than some weak baseline. In other words, we propose a weak learning condition and prove a boosting theory for ResNet under the weak learning condition. Our results apply to general multi-class ResNets. A generalization error bound based on margin theory is proved and suggests ResNet's resistant to overfitting under network with $l_1$ norm bounded weights.

Connected through hyperedge HE-1 (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"
    - "greedy layerwise learning can scale to imagenet"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "efficient forward architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "nas bench 101 towards reproducible neural architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "neural architecture optimization"
    - "path level network transformation for efficient architecture search"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "neural architecture search with bayesian optimisation and optimal transport"

Connected through hyperedge HE-3 (induced by source paper "sub architecture ensemble pruning in neural architecture search"):
  with members:
    - "improving neural architecture search image classifiers via ensemble learning"
    - "a survey on neural architecture search"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "regularized evolution for image classifier architecture search"
    - "darts differentiable architecture search"
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "blockqnn efficient block wise neural network architecture generation"
    - "efficient neural architecture search via parameter sharing"

Connected through hyperedge HE-4 (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"
    - "training neural networks using features replay"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "understanding synthetic gradients and decoupled neural interfaces"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-5 (induced by source paper "adagcn adaboosting graph convolutional networks into deep models"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "simplifying graph convolutional networks"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "pitfalls of graph neural network evaluation"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "n gcn multi scale graph convolution for semi supervised node classification"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "bootstrapped graph diffusions exposing the power of nonlinearity"

Connected through hyperedge HE-6 (induced by source paper "learning from noisy labels by regularized estimation of annotator confusion"):
  with members:
    - "joint optimization framework for learning with noisy labels"
    - "learning to reweight examples for robust deep learning"
    - "a probabilistic u net for segmentation of ambiguous images"
    - "learning from noisy singly labeled data"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "who said what modeling individual labelers improves classification"
    - "a survey on deep learning in medical image analysis"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "forest representation learning guided by margin distribution"):
  with members:
    - "multi layered gradient boosting decision trees"
    - "deep forest"
    - "deep forest towards an alternative to deep neural networks"
    - "autoencoder by forest"
    - "xgboost a scalable tree boosting system"
    - "on the doubt about margin explanation of boosting"

Connected through hyperedge HE-8 (induced by source paper "random sum product forests with residual links"):
  with members:
    - "spflow an easy and extensible library for deep probabilistic learning using sum product networks"
    - "density estimation using real nvp"
    - "deep residual learning for image recognition"
    - "sum product networks a new deep architecture"

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)