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: "fast rates for empirical risk minimization of strict saddle problems"
  Abstract: We derive bounds on the sample complexity of empirical risk minimization (ERM) in the context of minimizing non-convex risks that admit the strict saddle property. Recent progress in non-convex optimization has yielded efficient algorithms for minimizing such functions. Our results imply that these efficient algorithms are statistically stable and also generalize well. In particular, we derive fast rates which resemble the bounds that are often attained in the strongly convex setting. We specify our bounds to Principal Component Analysis and Independent Component Analysis. Our results and techniques may pave the way for statistical analyses of additional strict saddle problems.

Connected through hyperedge HE-1 (induced by source paper "understanding generalization in deep learning via tensor methods"):
  with members:
    - "the singular values of convolutional layers"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "generalization error in deep learning"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "tensor contraction layers for parsimonious deep nets"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"

Connected through hyperedge HE-2 (induced by source paper "solving empirical risk minimization in the current matrix multiplication time"):
  with members:
    - "nearly sample optimal sparse fourier transform in any dimension ripless and filterless"
    - "limits on all known and some unknown approaches to matrix multiplication"
    - "solving linear programs in the current matrix multiplication time"
    - "on the local minima of the empirical risk"
    - "limits on the universal method for matrix multiplication"
    - "guaranteed sufficient decrease for variance reduced stochastic gradient descent"
    - "empirical risk minimization for stochastic convex optimization o 1 n and o 1 n 2 type of risk bounds"
    - "variance reduced stochastic gradient descent with sufficient decrease"

Connected through hyperedge HE-3 (induced by source paper "understanding generalization through visualizations"):
  with members:
    - "identifying generalization properties in neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "averaging weights leads to wider optima and better generalization"
    - "certified defenses for data poisoning attacks"
    - "deep linear neural networks with arbitrary loss all local minima are global"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "data dependent stability of stochastic gradient descent"

Connected through hyperedge HE-4 (induced by source paper "learning in non convex games with an optimization oracle"):
  with members:
    - "introduction to online convex optimization"
    - "on convergence and stability of gans"
    - "efficient regret minimization in non convex games"
    - "oracle efficient online learning and auction design"
    - "the computational power of optimization in online learning"
    - "prediction by random walk perturbation"
    - "on the generalization ability of online learning algorithms for pairwise loss functions"

Connected through hyperedge HE-5 (induced by source paper "sharp minima can generalize for deep nets"):
  with members:
    - "eigenvalues of the hessian in deep learning singularity and beyond"
    - "wav2letter an end to end convnet based speech recognition system"
    - "understanding deep learning requires rethinking generalization"
    - "density estimation using real nvp"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "improved techniques for training gans"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "an empirical analysis of the optimization of deep network loss surfaces"

Connected through hyperedge HE-6 (induced by source paper "smooth sensitivity based approach for differentially private principal component analysis"):
  with members:
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm"
    - "efficient frequent directions algorithm for sparse matrices"
    - "robust shift and invert preconditioning faster and more sample efficient algorithms for eigenvector computation"
    - "convergence of stochastic gradient descent for pca"
    - "low rank approximation and regression in input sparsity time"
    - "beating randomized response on incoherent matrices"
    - "beyond worst case analysis in private singular vector computation"

Connected through hyperedge HE-7 (induced by source paper "data dependent stability of stochastic gradient descent"):
  with members:
    - "fast rates by transferring from auxiliary hypotheses"
    - "understanding deep learning requires rethinking generalization"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "train faster generalize better stability of stochastic gradient descent"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "the loss surfaces of multilayer networks"
    - "simultaneous model selection and optimization through parameter free stochastic learning"

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)