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: "training neural networks without gradients a scalable admm approach"
  Abstract: With the growing importance of large network models and enormous training datasets, GPUs have become increasingly necessary to train neural networks. This is largely because conventional optimization algorithms rely on stochastic gradient methods that don’t scale well to large numbers of cores in a cluster setting. Furthermore, the convergence of all gradient methods, including batch methods, suffers from common problems like saturation effects, poor conditioning, and saddle points. This paper explores an unconventional training method that uses alternating direction methods and Bregman iteration to train networks without gradient descent steps. The proposed method reduces the network training problem to a sequence of minimization substeps that can each be solved globally in closed form. The proposed method is advantageous because it avoids many of the caveats that make gradient methods slow on highly non-convex problems. The method exhibits strong scaling in the distributed setting, yielding linear speedups even when split over thousands of cores.

Connected through hyperedge HE-1 (induced by source paper "deep connections between learning from limited labels physical parameter estimation inspiration for regularization"):
  with members:
    - "symmetric block low rank layers for fully reversible multilevel neural networks"
    - "imexnet a forward stable deep neural network"
    - "fully hyperbolic convolutional neural networks"
    - "anodev2 a coupled neural ode evolution framework"
    - "leanconvnets low cost yet effective convolutional neural networks"
    - "anode unconditionally accurate memory efficient gradients for neural odes"
    - "regularization for deep learning a taxonomy"
    - "stable architectures for deep neural networks"

Connected through hyperedge HE-2 (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"
    - "deep convolutional networks on graph structured data"
    - "diffusion convolutional neural networks"

Connected through hyperedge HE-3 (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"
    - "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"
    - "stacked attention networks for image question answering"

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"
    - "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-5 (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-6 (induced by source paper "associated learning decomposing end to end backpropagation based on auto encoders and target propagation"):
  with members:
    - "decoupled parallel backpropagation with convergence guarantee"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "training neural networks using features replay"
    - "biologically motivated algorithms for propagating local target representations"
    - "conducting credit assignment by aligning local representations"
    - "deep supervised learning using local errors"
    - "dynamic routing between capsules"
    - "decoupled neural interfaces using synthetic gradients"

Connected through hyperedge HE-7 (induced by source paper "dante deep alternations for training neural networks"):
  with members:
    - "learning relu networks via alternating minimization"
    - "a provably correct algorithm for deep learning that actually works"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "recovery guarantees for one hidden layer neural networks"
    - "failures of gradient based deep learning"
    - "decoupled neural interfaces using synthetic gradients"
    - "efficient approaches for escaping higher order saddle points in non convex optimization"
    - "towards biologically plausible deep learning"

Connected through hyperedge HE-8 (induced by source paper "an adaptive remote stochastic gradient method for training neural networks"):
  with members:
    - "on the variance of the adaptive learning rate and beyond"
    - "on the convergence of adam and beyond"
    - "sadam a variant of adam for strongly convex functions"
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "scaling sgd batch size to 32k for imagenet training"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)