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: "mass an accelerated stochastic method for over parametrized learning"
  Abstract: In this paper we introduce MaSS (Momentum-added Stochastic Solver), an accelerated SGD method for optimizing over-parameterized networks. Our method is simple and efficient to implement and does not require changing parameters or computing full gradients in the course of optimization. We provide a detailed theoretical analysis for convergence and parameter selection including their dependence on the mini-batch size in the quadratic case. We also provide theoretical convergence results for a more general convex setting. #R##N#We provide an experimental evaluation showing strong performance of our method in comparison to Adam and SGD for several standard architectures of deep networks including ResNet, convolutional and fully connected networks. We also show its performance for convex kernel machines.

Connected through hyperedge HE-1 (induced by source paper "student specialization in deep relu networks with finite width and input dimension"):
  with members:
    - "luck matters understanding training dynamics of deep relu networks"
    - "measuring the intrinsic dimension of objective landscapes"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "the committee machine computational to statistical gaps in learning a two layers neural network"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "a convergence theory for deep learning via over parameterization"
    - "repr improved training of convolutional filters"

Connected through hyperedge HE-2 (induced by source paper "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"):
  with members:
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"
    - "the power of interpolation understanding the effectiveness of sgd in modern over parametrized learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "convergence analysis of two layer neural networks with relu activation"
    - "understanding deep learning requires rethinking generalization"
    - "guaranteed matrix completion via non convex factorization"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "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)