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: "small nonlinearities in activation functions create bad local minima in neural networks"
  Abstract: We investigate the loss surface of neural networks. We prove that even for one-hidden-layer networks with "slightest" nonlinearity, the empirical risks have spurious local minima in most cases. Our results thus indicate that in general "no spurious local minima" is a property limited to deep linear networks, and insights obtained from linear networks may not be robust. Specifically, for ReLU(-like) networks we constructively prove that for almost all practical datasets there exist infinitely many local minima. We also present a counterexample for more general activations (sigmoid, tanh, arctan, ReLU, etc.), for which there exists a bad local minimum. Our results make the least restrictive assumptions relative to existing results on spurious local optima in neural networks. We complete our discussion by presenting a comprehensive characterization of global optimality for deep linear networks, which unifies other results on this topic.

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"
    - "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"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"

Connected through hyperedge HE-2 (induced by source paper "geometry and topology of deep neural networks decision boundaries"):
  with members:
    - "understanding global loss landscape of one hidden layer relu neural networks"
    - "barcodes as summary of objective function s topology"
    - "depth creates no more spurious local minima"
    - "on connected sublevel sets in deep learning"
    - "elimination of all bad local minima in deep learning"
    - "on decision regions of narrow deep 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-3 (induced by source paper "global convergence of deep networks with one wide layer followed by pyramidal topology"):
  with members:
    - "how much over parameterization is sufficient to learn deep relu networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "the effect of network width on stochastic gradient descent and generalization an empirical study"
    - "polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks"
    - "global convergence of adaptive gradient methods for an over parameterized neural network"
    - "on connected sublevel sets in deep learning"
    - "neural networks learning and memorization with almost no over parameterization"
    - "over parameterized deep neural networks have no strict local minima for any continuous activations"

Connected through hyperedge HE-4 (induced by source paper "a generalization theory of gradient descent for learning over parameterized deep relu networks"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "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"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "towards understanding the role of over parametrization in generalization of neural networks"

Connected through hyperedge HE-5 (induced by source paper "generalization error bounds of gradient descent for learning over parameterized deep relu networks"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "an improved analysis of training over parameterized deep neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "tight sample complexity of learning one hidden layer convolutional neural networks"
    - "generalization bounds of stochastic gradient descent for wide and deep neural networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "learning one hidden layer neural networks under general input distributions"
    - "gradient descent finds global minima of deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "hessian based analysis of sgd for deep nets dynamics and generalization"):
  with members:
    - "escaping saddle points with adaptive gradient methods"
    - "measurements of three level hierarchical structure in the outliers in the spectrum of deepnet hessians"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "on the convergence of adam and beyond"
    - "positively scale invariant flatness of relu neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "exponential convergence time of gradient descent for one dimensional deep linear neural networks"

Connected through hyperedge HE-7 (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"
    - "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"

Connected through hyperedge HE-8 (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"
    - "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"
    - "gradient descent aligns the layers of deep linear 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)