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: "algorithmic regularization in over parameterized matrix recovery"
  Abstract: We study the problem of recovering a low-rank matrix $X^\star$ from linear measurements using an over-parameterized model. We parameterize the rank-$r$ matrix $X^\star$ by $UU^\top$ where $U\in \mathbb{R}^{d\times d}$ is a square matrix, whereas the number of linear measurements is much less than $d^2$. We show that with $\tilde{O}(dr^{2})$ random linear measurements, the gradient descent on the squared loss, starting from a small initialization, recovers $X^\star$ approximately in $\tilde{O}(\sqrt{r})$ iterations. The results solve the conjecture of Gunasekar et al. under the restricted isometry property, and demonstrate that the training algorithm can provide an implicit regularization for non-linear matrix factorization models.

Connected through hyperedge HE-1 (induced by source paper "revisiting landscape analysis in deep neural networks eliminating decreasing paths to infinity"):
  with members:
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "on exact computation with an infinitely wide neural net"
    - "elimination of all bad local minima in deep learning"
    - "on the convergence of adaptive gradient methods for nonconvex optimization"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "on the connection between learning two layers neural networks and tensor decomposition"
    - "learning one hidden layer neural networks under general input distributions"
    - "over parameterized deep neural networks have no strict local minima for any continuous activations"

Connected through hyperedge HE-2 (induced by source paper "beyond linearization on quadratic and higher order approximation of wide neural networks"):
  with members:
    - "simple and effective regularization methods for training on noisily labeled data with generalization guarantee"
    - "kernel and deep regimes in overparametrized models"
    - "on nonconvex optimization for machine learning gradients stochasticity and saddle points"
    - "generalization error bounds of gradient descent for learning over parameterized deep relu networks"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "on the power and limitations of random features for understanding neural networks"

Connected through hyperedge HE-3 (induced by source paper "on the generalization gap in reparameterizable reinforcement learning"):
  with members:
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "reinforcement learning with perturbed rewards"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "identifying generalization properties in neural networks"
    - "generalization and regularization in dqn"
    - "a study on overfitting in deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "towards explaining the regularization effect of initial large learning rate in training neural networks"):
  with members:
    - "sgd on neural networks learns functions of increasing complexity"
    - "the step decay schedule a near optimal geometrically decaying learning rate procedure"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "what can resnet learn efficiently going beyond kernels"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"

Connected through hyperedge HE-5 (induced by source paper "improved sample complexities for deep networks and robust classification via an all layer margin"):
  with members:
    - "a hessian based complexity measure for deep networks"
    - "learning imbalanced datasets with label distribution aware margin loss"
    - "vc classes are adversarially robustly learnable but only improperly"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "theoretically principled trade off between robustness and accuracy"
    - "gradient descent maximizes the margin of homogeneous neural networks"
    - "adversarial training can hurt generalization"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"

Connected through hyperedge HE-6 (induced by source paper "fixup initialization residual learning without normalization"):
  with members:
    - "scaling neural machine translation"
    - "shakedrop regularization for deep residual learning"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice"
    - "attention is all you need"
    - "mixup beyond empirical risk minimization"
    - "the shattered gradients problem if resnets are the answer then what is the question"
    - "l2 regularization versus batch and weight normalization"

Connected through hyperedge HE-7 (induced by source paper "can sgd learn recurrent neural networks with provable generalization"):
  with members:
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "on the convergence rate of training recurrent neural networks"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"
    - "theoretical properties of the global optimizer of two layer neural network"
    - "recent advances in recurrent neural networks"
    - "a convergence theory for deep learning via over parameterization"
    - "on generalization bounds of a family of recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "on the power and limitations of random features for understanding neural networks"):
  with members:
    - "can sgd learn recurrent neural networks with provable generalization"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "why do larger models generalize better a theoretical perspective via the xor problem"
    - "a convergence theory for deep learning via over parameterization"
    - "on the power of over parametrization in neural networks with quadratic activation"

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)