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: "an empirical analysis of the optimization of deep network loss surfaces"
  Abstract: The success of deep neural networks hinges on our ability to accurately and efficiently optimize high-dimensional, non-convex functions. In this paper, we empirically investigate the loss functions of state-of-the-art networks, and how commonly-used stochastic gradient descent variants optimize these loss functions. To do this, we visualize the loss function by projecting them down to low-dimensional spaces chosen based on the convergence points of different optimization algorithms. Our observations suggest that optimization algorithms encounter and choose different descent directions at many saddle points to find different final weights. Based on consistency we observe across re-runs of the same stochastic optimization algorithm, we hypothesize that each optimization algorithm makes characteristic choices at these saddle points.

Connected through hyperedge HE-1 (induced by source paper "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "recovering from random pruning on the plasticity of deep convolutional neural networks"
    - "training wide residual networks for deployment using a single bit for each weight"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "network compression using correlation analysis of layer responses"
    - "data driven sparse structure selection for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "error feedback fixes signsgd and other gradient compression schemes"):
  with members:
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "a study on overfitting in deep reinforcement learning"
    - "sparsified sgd with memory"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"
    - "signsgd compressed optimisation for non convex problems"
    - "error compensated quantized sgd and its applications to large scale distributed optimization"

Connected through hyperedge HE-3 (induced by source paper "partitioned integrators for thermodynamic parameterization of neural networks"):
  with members:
    - "lca loss change allocation for neural network training"
    - "understanding generalization through visualizations"
    - "three factors influencing minima in sgd"
    - "empirical analysis of the hessian of over parametrized neural networks"
    - "improving palliative care with deep learning"
    - "understanding deep learning requires rethinking generalization"
    - "layer specific adaptive learning rates for deep networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-4 (induced by source paper "multi class classification without multi class labels"):
  with members:
    - "classification from pairwise similarity and unlabeled data"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "visualizing the loss landscape of neural nets"
    - "learning to cluster in order to transfer across domains and tasks"
    - "semi supervised kernel metric learning using relative comparisons"
    - "identity mappings in deep residual networks"
    - "matching networks for one shot learning"

Connected through hyperedge HE-5 (induced by source paper "model agnostic meta learning using runge kutta methods"):
  with members:
    - "es maml simple hessian free meta learning"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "on first order meta learning algorithms"
    - "meta learning with hessian free approach in deep neural nets training"
    - "how to train your maml"
    - "probabilistic model agnostic meta learning"
    - "meta sgd learning to learn quickly for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-6 (induced by source paper "quantitatively evaluating gans with divergences proposed for training"):
  with members:
    - "on convergence and stability of gans"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "began boundary equilibrium generative adversarial networks"
    - "generative adversarial parallelization"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "comparison of maximum likelihood and gan based training of real nvps"
    - "the numerics of gans"
    - "mmd gan towards deeper understanding of moment matching network"

Connected through hyperedge HE-7 (induced by source paper "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"):
  with members:
    - "regularization for deep learning a taxonomy"
    - "allennlp a deep semantic natural language processing platform"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "three factors influencing minima in sgd"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "spectrally normalized margin bounds for neural networks"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "entropy sgd biasing gradient descent into wide valleys"

Connected through hyperedge HE-8 (induced by source paper "spi optimizer an integral separated pi controller for stochastic optimization"):
  with members:
    - "on the convergence of adam and beyond"
    - "surfacenet an end to end 3d neural network for multiview stereopsis"
    - "neural optimizer search with reinforcement learning"
    - "densely connected convolutional networks"
    - "wide residual networks"
    - "deep residual learning for image recognition"
    - "deep learning for surface material classification using haptic and visual information"
    - "adam a method for stochastic optimization"

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)