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: "distributed optimization of deeply nested systems"
  Abstract: In science and engineering, intelligent processing of complex signals such as images, sound or language is often performed by a parameterized hierarchy of nonlinear processing layers, sometimes biologically inspired. Hierarchical systems (or, more generally, nested systems) offer a way to generate complex mappings using simple stages. Each layer performs a different operation and achieves an ever more sophisticated representation of the input, as, for example, in an deep artificial neural network, an object recognition cascade in computer vision or a speech front-end processing. Joint estimation of the parameters of all the layers and selection of an optimal architecture is widely considered to be a difficult numerical nonconvex optimization problem, difficult to parallelize for execution in a distributed computation environment, and requiring significant human expert effort, which leads to suboptimal systems in practice. We describe a general mathematical strategy to learn the parameters and, to some extent, the architecture of nested systems, called the method of auxiliary coordinates (MAC). This replaces the original problem involving a deeply nested function with a constrained problem involving a different function in an augmented space without nesting. The constrained problem may be solved with penalty-based methods using alternating optimization over the parameters and the auxiliary coordinates. MAC has provable convergence, is easy to implement reusing existing algorithms for single layers, can be parallelized trivially and massively, applies even when parameter derivatives are not available or not desirable, and is competitive with state-of-the-art nonlinear optimizers even in the serial computation setting, often providing reasonable models within a few iterations.

Connected through hyperedge HE-1 (induced by source paper "layer wise conditioning analysis in exploring the learning dynamics of dnns"):
  with members:
    - "a mean field theory of batch normalization"
    - "mean field analysis of batch normalization"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "l1 norm batch normalization for efficient training of deep neural networks"
    - "group normalization"
    - "the full spectrum of deep net hessians at scale dynamics with sample size"
    - "large scale gan training for high fidelity natural image synthesis"
    - "proximal backpropagation"

Connected through hyperedge HE-2 (induced by source paper "joint parameter and bandwidth allocation for improving the efficiency of partitioned edge learning"):
  with members:
    - "device scheduling with fast convergence for wireless federated learning"
    - "energy efficient radio resource allocation for federated edge learning"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "energy efficient federated learning over wireless communication networks"
    - "accelerating dnn training in wireless federated edge learning system"
    - "hierarchical federated learning across heterogeneous cellular networks"
    - "scheduling policies for federated learning in wireless networks"
    - "federated learning in mobile edge networks a comprehensive survey"

Connected through hyperedge HE-3 (induced by source paper "a lagrangian approach to information propagation in graph neural networks"):
  with members:
    - "how powerful are graph neural networks"
    - "anonymous walk embeddings"
    - "geometric deep learning going beyond euclidean data"
    - "gated graph sequence neural networks"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "training neural networks without gradients a scalable admm approach"
    - "deep convolutional networks on graph structured data"

Connected through hyperedge HE-4 (induced by source paper "local propagation in constraint based neural network"):
  with members:
    - "learning deep generative models of graphs"
    - "attention is all you need"
    - "identity mappings in deep residual networks"
    - "training neural networks without gradients a scalable admm approach"
    - "deep learning in neural networks"
    - "deep convolutional networks on graph structured data"
    - "deep residual learning for image recognition"
    - "ask attend and answer exploring question guided spatial attention for visual question answering"

Connected through hyperedge HE-5 (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-6 (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"
    - "learning deep resnet blocks sequentially using boosting theory"

Connected through hyperedge HE-7 (induced by source paper "accelerating neural odes with spectral elements"):
  with members:
    - "anode unconditionally accurate memory efficient gradients for neural odes"
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "physics guided recurrent neural networks for modeling dynamical systems application to monitoring water temperature and quality in lakes"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "neural ordinary differential equations"
    - "nais net stable deep networks from non autonomous differential equations"
    - "lstm a search space odyssey"

Connected through hyperedge HE-8 (induced by source paper "snode spectral discretization of neural odes for system identification"):
  with members:
    - "anode unconditionally accurate memory efficient gradients for neural odes"
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "physics guided recurrent neural networks for modeling dynamical systems application to monitoring water temperature and quality in lakes"
    - "stable architectures for deep neural networks"
    - "nais net stable deep networks from non autonomous differential equations"
    - "lstm a search space odyssey"
    - "recurrent highway networks"
    - "highway and residual networks learn unrolled iterative estimation"

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)