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 "disentangling feature and lazy training in deep neural networks"):
  Abstract: Two distinct limits for deep learning have been derived as the network width $h\rightarrow \infty$, depending on how the weights of the last layer scale with $h$. In the Neural Tangent Kernel (NTK) limit, the dynamics becomes linear in the weights and is described by a frozen kernel $\Theta$ (the NTK). By contrast, in the Mean Field limit, the dynamics can be expressed in terms of the distribution of the parameters associated to a neuron, that follows a partial differential equation. In this work we consider deep networks where the weights in the last layer scale as $\alpha h^{-1/2}$ at initialization. By varying $\alpha$ and $h$, we probe the crossover between the two limits. We observe two regimes that we call "lazy training" and "feature training". In the lazy-training regime, the dynamics is almost linear and the NTK does barely change after initialization. The feature-training regime includes the mean-field formulation as a limiting case and is characterized by a kernel that evolves in time, and thus learns some features. We perform numerical experiments on MNIST, Fashion-MNIST, EMNIST and CIFAR10 and consider various architectures. We find that: (i) The two regimes are separated by an $\alpha^*$ that scales as $\frac{1}{\sqrt{h}}$. (ii) Network architecture and data structure play an important role in determining which regime is better: in our tests, fully-connected networks generally perform better in the lazy-training regime (except when we reduce the dataset via PCA), and we provide an example of a convolutional network that achieves a lower error in the feature-training regime. (iii) In both regimes, the fluctuations $\delta F$ induced by initial conditions on the learned function decay as $\delta F \sim 1/\sqrt{h}$, leading to a performance that increases with $h$. (iv) In the feature-training regime we identify a time scale $t_1 \sim \sqrt{h}\alpha$

Includes members:
  Member "a jamming transition from under to over parametrization affects generalization in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "self adaptive training beyond empirical risk minimization")
  Member "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "asymptotics of wide networks from feynman diagrams":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the infinite width limit of neural networks with a standard parameterization")
  Member "the effect of network width on stochastic gradient descent and generalization an empirical study":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "batch normalization biases deep residual networks towards shallow paths")
  Member "the asymptotic spectrum of the hessian of dnn throughout training":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "disentangling feature and lazy learning in deep neural networks an empirical study")
  Member "mean field limit of the learning dynamics of multilayer neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a rigorous framework for the mean field limit of multilayer neural networks")
  Member "on exact computation with an infinitely wide neural net":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "a modern take on the bias variance tradeoff in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distilled hierarchical neural ensembles with adaptive inference cost")

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)