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: "quasi potential as an implicit regularizer for the loss function in the stochastic gradient descent"
  Abstract: We interpret the variational inference of the Stochastic Gradient Descent (SGD) as minimizing a new potential function named the \textit{quasi-potential}. We analytically construct the quasi-potential function in the case when the loss function is convex and admits only one global minimum point. We show in this case that the quasi-potential function is related to the noise covariance structure of SGD via a partial differential equation of Hamilton-Jacobi type. This relation helps us to show that anisotropic noise leads to faster escape than isotropic noise. We then consider the dynamics of SGD in the case when the loss function is non-convex and admits several different local minima. In this case, we demonstrate an example that shows how the noise covariance structure plays a role in "implicit regularization", a phenomenon in which SGD favors some particular local minimum points. This is done through the relation between the noise covariance structure and the quasi-potential function. Our analysis is based on Large Deviations Theory (LDT), and they are validated by numerical experiments.

Connected through hyperedge HE-1 (induced by source paper "the multiplicative noise in stochastic gradient descent data dependent regularization continuous and discrete approximation"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "uniform in time weak error analysis for stochastic gradient descent algorithms via diffusion approximation"
    - "an empirical study on regularization of deep neural networks by local rademacher complexity"
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"
    - "three factors influencing minima in sgd"
    - "an alternative view when does sgd escape local minima"
    - "escaping saddles with stochastic gradients"
    - "generalization bounds of sgld for non convex learning two theoretical viewpoints"

Connected through hyperedge HE-2 (induced by source paper "on the noisy gradient descent that generalizes as sgd"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "hessian based analysis of sgd for deep nets dynamics and generalization"
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"
    - "generalization bounds of stochastic gradient descent for wide and deep neural networks"
    - "three factors influencing minima in sgd"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "opening the black box of deep neural networks via information"
    - "understanding deep learning requires rethinking generalization"

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)