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: "deep linear neural networks with arbitrary loss all local minima are global"
  Abstract: We consider deep linear networks with arbitrary differentiable loss. We provide a short and elementary proof of the following fact: all local minima are global minima if each hidden layer is wider than either the input or output layer.

Connected through hyperedge HE-1 (induced by source paper "understanding global loss landscape of one hidden layer relu neural networks"):
  with members:
    - "depth creates no more spurious local minima"
    - "on connected sublevel sets in deep learning"
    - "elimination of all bad local minima in deep learning"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "learning one hidden layer neural networks under general input distributions"
    - "over parameterized deep neural networks have no strict local minima for any continuous activations"
    - "gradient descent finds global minima of deep neural networks"
    - "on the loss landscape of a class of deep neural networks with no bad local valleys"

Connected through hyperedge HE-2 (induced by source paper "de randomized pac bayes margin bounds applications to non convex and non smooth predictors"):
  with members:
    - "hessian based analysis of sgd for deep nets dynamics and generalization"
    - "size free generalization bounds for convolutional neural networks"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "algorithm dependent generalization bounds for overparameterized deep residual networks"
    - "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"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "on tighter generalization bound for deep neural networks cnns resnets and beyond"

Connected through hyperedge HE-3 (induced by source paper "provable benefit of orthogonal initialization in optimizing deep linear networks"):
  with members:
    - "width provably matters in optimization for deep linear neural networks"
    - "dynamical isometry and a mean field theory of lstms and grus"
    - "exponential convergence time of gradient descent for one dimensional deep linear neural networks"
    - "stable architectures for deep neural networks"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice"
    - "depth creates no bad local minima"
    - "global optimality conditions for deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "convex duality of deep neural networks"):
  with members:
    - "width provably matters in optimization for deep linear neural networks"
    - "implicit regularization in deep matrix factorization"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "exponential convergence time of gradient descent for one dimensional deep linear neural networks"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "in search of the real inductive bias on the role of implicit regularization in deep learning"

Connected through hyperedge HE-5 (induced by source paper "deep learning based wireless resource allocation with application to vehicular networks"):
  with members:
    - "learning to branch accelerating resource allocation in wireless networks"
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "learning to detect"
    - "learning optimal resource allocations in wireless systems"
    - "deep learning based end to end wireless communication systems with conditional gan as unknown channel"
    - "distributed power control for large energy harvesting networks a multi agent deep reinforcement learning approach"
    - "power allocation in multi user cellular networks deep reinforcement learning approaches"
    - "toward intelligent vehicular networks a machine learning framework"

Connected through hyperedge HE-6 (induced by source paper "sub optimal local minima exist for almost all over parameterized neural networks"):
  with members:
    - "an improved analysis of training over parameterized deep neural networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "depth creates no more spurious local minima"
    - "on connected sublevel sets in deep learning"
    - "on the connection between learning two layers neural networks and tensor decomposition"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "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-7 (induced by source paper "truth or backpropaganda an empirical investigation of deep learning theory"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "fixup initialization residual learning without normalization"
    - "understanding generalization through visualizations"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "gradient descent finds global minima of deep neural networks"
    - "the singular values of convolutional layers"
    - "neural tangent kernel convergence and generalization in neural networks"

Connected through hyperedge HE-8 (induced by source paper "on connected sublevel sets in deep learning"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "on the loss landscape of a class of deep neural networks with no bad local valleys"
    - "understanding the loss surface of neural networks for binary classification"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"

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)