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: "the power of normalization faster evasion of saddle points"
  Abstract: A commonly used heuristic in non-convex optimization is Normalized Gradient Descent (NGD) - a variant of gradient descent in which only the direction of the gradient is taken into account and its magnitude ignored. We analyze this heuristic and show that with carefully chosen parameters and noise injection, this method can provably evade saddle points. We establish the convergence of NGD to a local minimum, and demonstrate rates which improve upon the fastest known first order algorithm due to Ge e al. (2015). #R##N#The effectiveness of our method is demonstrated via an application to the problem of online tensor decomposition; a task for which saddle point evasion is known to result in convergence to global minima.

Connected through hyperedge HE-1 (induced by source paper "automl zero evolving machine learning algorithms from scratch"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "nas evaluation is frustratingly hard"
    - "towards modular and programmable architecture search"
    - "weight agnostic neural networks"
    - "exploring randomly wired neural networks for image recognition"
    - "the evolved transformer"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"

Connected through hyperedge HE-2 (induced by source paper "on nonconvex optimization for machine learning gradients stochasticity and saddle points"):
  with members:
    - "finding local minima via stochastic nested variance reduction"
    - "escaping saddles with stochastic gradients"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "stochastic cubic regularization for fast nonconvex optimization"
    - "neon2 finding local minima via first order oracles"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "a generic approach for escaping saddle points"

Connected through hyperedge HE-3 (induced by source paper "escaping saddle points with adaptive gradient methods"):
  with members:
    - "on the convergence of adam and beyond"
    - "on the convergence of adaptive gradient methods for nonconvex optimization"
    - "a sufficient condition for convergences of adam and rmsprop"
    - "the case for full matrix adaptive regularization"
    - "escaping saddle points in constrained optimization"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "on the convergence of a class of adam type algorithms for non convex optimization"
    - "variants of rmsprop and adagrad with logarithmic regret bounds"

Connected through hyperedge HE-4 (induced by source paper "the geometry of sign gradient descent"):
  with members:
    - "hessian based analysis of sgd for deep nets dynamics and generalization"
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "ellipsoidal trust region methods and the marginal value of hessian information for neural network training"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "block normalized gradient method an empirical study for training deep neural network"
    - "the full spectrum of deep net hessians at scale dynamics with sample size"
    - "dissecting adam the sign magnitude and variance of stochastic gradients"

Connected through hyperedge HE-5 (induced by source paper "a deterministic approach to avoid saddle points"):
  with members:
    - "laplacian smoothing gradient descent"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "how to escape saddle points efficiently"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "matrix completion has no spurious local minimum"
    - "a geometric analysis of phase retrieval"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"

Connected through hyperedge HE-6 (induced by source paper "on the power of over parametrization in neural networks with quadratic activation"):
  with members:
    - "generalization error bounds for noisy iterative algorithms"
    - "learning one hidden layer neural networks with landscape design"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "fisher rao metric geometry and complexity of neural networks"
    - "generalization bounds of sgld for non convex learning two theoretical viewpoints"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"

Connected through hyperedge HE-7 (induced by source paper "defending against saddle point attack in byzantine robust distributed learning"):
  with members:
    - "byzantine stochastic gradient descent"
    - "high dimensional robust sparse regression"
    - "securing distributed machine learning in high dimensions"
    - "generalized byzantine tolerant sgd"
    - "efficient algorithms for outlier robust regression"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "sever a robust meta algorithm for stochastic optimization"
    - "being robust in high dimensions can be practical"

Connected through hyperedge HE-8 (induced by source paper "finding local minima via stochastic nested variance reduction"):
  with members:
    - "stochastic variance reduced cubic regularized newton method"
    - "stochastic nested variance reduction for nonconvex optimization"
    - "escaping saddles with stochastic gradients"
    - "stochastic cubic regularization for fast nonconvex optimization"
    - "neon2 finding local minima via first order oracles"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "a generic approach for escaping saddle points"
    - "sub sampled cubic regularization for non convex 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)