human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "nearly minimal over parametrization of shallow neural networks"):
  Abstract: A recent line of work has shown that an overparametrized neural network can perfectly fit the training data, an otherwise often intractable nonconvex optimization problem. For (fully-connected) shallow networks, in the best case scenario, the existing theory requires quadratic over-parametrization as a function of the number of training samples. This paper establishes that linear overparametrization is sufficient to fit the training data, using a simple variant of the (stochastic) gradient descent. Crucially, unlike several related works, the training considered in this paper is not limited to the lazy regime in the sense cautioned against in [1, 2]. Beyond shallow networks, the framework developed in this work for over-parametrization is applicable to a variety of learning problems.

Includes members:
  Member "quadratic suffices for over parametrization via matrix chernoff bound":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "over parameterized adversarial training an analysis overcoming the curse of dimensionality")
  Member "nonconvex optimization meets low rank matrix factorization an overview":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "an improved analysis of training over parameterized deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "convergence of end to end training in deep unsupervised contrasitive learning")
  Member "towards moderate overparameterization global convergence guarantees for training shallow neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "trainability and data dependent initialization of over parameterized relu neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generating accurate pseudo labels via hermite polynomials for ssl confidently")
  Member "on learning over parameterized neural networks a functional approximation prospective":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mean field analysis of two layer neural networks non asymptotic rates and generalization bounds")
  Member "global convergence of adaptive gradient methods for an over parameterized neural network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "convergence of end to end training in deep unsupervised contrasitive learning")
  Member "fine grained analysis of optimization and generalization for overparameterized two layer neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "disentangling adaptive gradient methods from learning rates")

Question: Please tell me which class the source paper 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)