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: "explicitizing an implicit bias of the frequency principle in two layer neural networks"
  Abstract: It remains a puzzle that why deep neural networks (DNNs), with more parameters than samples, often generalize well. An attempt of understanding this puzzle is to discover implicit biases underlying the training process of DNNs, such as the Frequency Principle (F-Principle), i.e., DNNs often fit target functions from low to high frequencies. Inspired by the F-Principle, we propose an effective model of linear F-Principle (LFP) dynamics which accurately predicts the learning results of two-layer ReLU neural networks (NNs) of large widths. This LFP dynamics is rationalized by a linearized mean field residual dynamics of NNs. Importantly, the long-time limit solution of this LFP dynamics is equivalent to the solution of a constrained optimization problem explicitly minimizing an FP-norm, in which higher frequencies of feasible solutions are more heavily penalized. Using this optimization formulation, an a priori estimate of the generalization error bound is provided, revealing that a higher FP-norm of the target function increases the generalization error. Overall, by explicitizing the implicit bias of the F-Principle as an explicit penalty for two-layer NNs, our work makes a step towards a quantitative understanding of the learning and generalization of general DNNs.

Connected through hyperedge HE-1 (induced by source paper "a fine grained spectral perspective on neural networks"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "frequency principle fourier analysis sheds light on deep neural networks"
    - "a mean field theory of batch normalization"
    - "towards characterizing divergence in deep q learning"
    - "the convergence rate of neural networks for learned functions of different frequencies"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "training behavior of deep neural network in frequency domain"

Connected through hyperedge HE-2 (induced by source paper "a priori generalization error for two layer relu neural network through minimum norm solution"):
  with members:
    - "frequency principle fourier analysis sheds light on deep neural networks"
    - "a type of generalization error induced by initialization in deep neural networks"
    - "multi scale deep neural networks for solving high dimensional pdes"
    - "theory of the frequency principle for general deep neural networks"
    - "on exact computation with an infinitely wide neural net"
    - "a phase shift deep neural network for high frequency approximation and wave problems"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a comparative analysis of the optimization and generalization property of two layer neural network and random feature models under gradient descent dynamics"

Connected through hyperedge HE-3 (induced by source paper "theory of the frequency principle for general deep neural networks"):
  with members:
    - "frequency principle fourier analysis sheds light on deep neural networks"
    - "meta learners learning dynamics are unlike learners"
    - "training behavior of deep neural network in frequency domain"
    - "frequency principle in deep learning with general loss functions and its potential application"
    - "understanding training and generalization in deep learning by fourier analysis"
    - "nonlinear collaborative scheme for deep neural networks"
    - "recent trends in deep learning based natural language processing"
    - "deep speech 2 end to end speech recognition in english and mandarin"

Connected through hyperedge HE-4 (induced by source paper "multi scale deep neural networks for solving high dimensional pdes"):
  with members:
    - "frequency principle fourier analysis sheds light on deep neural networks"
    - "theory of the frequency principle for general deep neural networks"
    - "a phase shift deep neural network for high frequency approximation and wave problems"
    - "training behavior of deep neural network in frequency domain"
    - "understanding training and generalization in deep learning by fourier analysis"
    - "deep learning approximation for stochastic control problems"
    - "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)