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: "the global optimization geometry of shallow linear neural networks"
  Abstract: We examine the squared error loss landscape of shallow linear neural networks. By utilizing a regularizer on the training samples, we show---with significantly milder assumptions than previous works---that the corresponding optimization problems have benign geometric properties: there are no spurious local minima and the Hessian at every saddle point has at least one negative eigenvalue. This means that at every saddle point there is a directional negative curvature which algorithms can utilize to further decrease the objective value. These geometric properties imply that many local search algorithms---including gradient descent, which is widely utilized for training neural networks---can provably solve the training problem with global convergence. The additional regularizer has no effect on the global minimum value; rather, it plays a useful role in shrinking the set of critical points. Experiments show that this additional regularizer also speeds the convergence of iterative algorithms for solving the training optimization problem in certain cases.

Connected through hyperedge HE-1 (induced by source paper "coherent gradients an approach to understanding generalization in gradient descent based optimization"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "stiffness a new perspective on generalization in neural networks"
    - "circuit based intrinsic methods to detect overfitting"
    - "the impact of neural network overparameterization on gradient confusion and stochastic gradient descent"
    - "deep learning is robust to massive label noise"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"

Connected through hyperedge HE-2 (induced by source paper "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"):
  with members:
    - "generalization guarantees for neural networks via harnessing the low rank structure of the jacobian"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "high dimensional robust sparse regression"
    - "stochastic gradient mirror descent minimax optimality and implicit regularization"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "deep learning is robust to massive label noise"
    - "learning with bad training data via iterative trimmed loss minimization"

Connected through hyperedge HE-3 (induced by source paper "towards moderate overparameterization global convergence guarantees for training shallow neural networks"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "on the local minima of the empirical risk"
    - "gradient descent finds global minima of deep neural networks"
    - "overparameterized nonlinear learning gradient descent takes the shortest path"
    - "stochastic gradient descent learns state equations with nonlinear activations"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "gradient descent aligns the layers of deep linear networks"

Connected through hyperedge HE-4 (induced by source paper "learning action representations for reinforcement learning"):
  with members:
    - "the natural language of actions"
    - "combined reinforcement learning via abstract representations"
    - "learning to factor policies and action value functions factored action space representations for deep reinforcement learning"
    - "curiosity driven exploration by self supervised prediction"
    - "proximal policy optimization algorithms"
    - "label embedding for image classification"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "loss is its own reward self supervision for reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "learning one hidden layer neural networks via provable gradient descent with random initialization"):
  with members:
    - "nonconvex demixing from bilinear measurements"
    - "nonconvex matrix factorization from rank one measurements"
    - "blind over the air computation and data fusion via provable wirtinger flow"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"
    - "how to escape saddle points efficiently"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "algorithmic regularization in over parameterized matrix sensing and neural networks with quadratic activations"

Connected through hyperedge HE-6 (induced by source paper "decoupling gating from linearity"):
  with members:
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "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"
    - "on the connection between learning two layers neural networks and tensor decomposition"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "overparameterized nonlinear learning gradient descent takes the shortest path"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "a convergence theory for deep learning via over parameterization"

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)