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: "implicit regularization of discrete gradient dynamics in linear neural networks"
  Abstract: When optimizing over-parameterized models, such as deep neural networks, a large set of parameters can achieve zero training error. In such cases, the choice of the optimization algorithm and its respective hyper-parameters introduces biases that will lead to convergence to specific minimizers of the objective. Consequently, this choice can be considered as an implicit regularization for the training of over-parametrized models. In this work, we push this idea further by studying the discrete gradient dynamics of the training of a two-layer linear network with the least-squares loss. Using a time rescaling, we show that, with a vanishing initialization and a small enough step size, this dynamics sequentially learns the solutions of a reduced-rank regression with a gradually increasing rank.

Connected through hyperedge HE-1 (induced by source paper "a sample complexity separation between non convex and convex meta learning"):
  with members:
    - "decoupling adaptation from modeling with meta optimizers for meta learning"
    - "adaptive gradient based meta learning methods"
    - "learning to learn stochastic gradient descent with biased regularization"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "meta learning with implicit gradients"
    - "on the convergence theory of gradient based model agnostic meta learning algorithms"
    - "a mathematical theory of semantic development in deep neural networks"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-2 (induced by source paper "gradient descent maximizes the margin of homogeneous neural networks"):
  with members:
    - "a refined primal dual analysis of the implicit bias"
    - "implicit regularization in deep matrix factorization"
    - "fixup initialization residual learning without normalization"
    - "on exact computation with an infinitely wide neural net"
    - "implicit regularization for deep neural networks driven by an ornstein uhlenbeck like process"
    - "sorting out lipschitz function approximation"
    - "gradient descent finds global minima of deep neural networks"
    - "implicit bias of gradient descent on linear convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "implicit regularization in deep matrix factorization"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "width provably matters in optimization for deep linear neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "algorithmic regularization in learning deep homogeneous models layers are automatically balanced"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "exploring generalization in deep learning"

Connected through hyperedge HE-4 (induced by source paper "the implicit bias of depth how incremental learning drives generalization"):
  with members:
    - "sgd on neural networks learns functions of increasing complexity"
    - "kernel and deep regimes in overparametrized models"
    - "gradient dynamics of shallow univariate relu networks"
    - "the convergence rate of neural networks for learned functions of different frequencies"
    - "implicit regularization in deep matrix factorization"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "on the learning dynamics of deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "how implicit regularization of neural networks affects the learned function part i"):
  with members:
    - "implicit regularization in over parameterized neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "theory iiib generalization in deep networks"
    - "implicit regularization in deep learning"
    - "adam a method for stochastic optimization"
    - "in search of the real inductive bias on the role of implicit regularization in deep learning"

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)