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: "learning halfspaces and neural networks with random initialization"
  Abstract: We study non-convex empirical risk minimization for learning halfspaces and neural networks. For loss functions that are $L$-Lipschitz continuous, we present algorithms to learn halfspaces and multi-layer neural networks that achieve arbitrarily small excess risk $\epsilon>0$. The time complexity is polynomial in the input dimension $d$ and the sample size $n$, but exponential in the quantity $(L/\epsilon^2)\log(L/\epsilon)$. These algorithms run multiple rounds of random initialization followed by arbitrary optimization steps. We further show that if the data is separable by some neural network with constant margin $\gamma>0$, then there is a polynomial-time algorithm for learning a neural network that separates the training data with margin $\Omega(\gamma)$. As a consequence, the algorithm achieves arbitrary generalization error $\epsilon>0$ with ${\rm poly}(d,1/\epsilon)$ sample and time complexity. We establish the same learnability result when the labels are randomly flipped with probability $\eta<1/2$.

Connected through hyperedge HE-1 (induced by source paper "learning two layer networks with multinomial activation and high thresholds"):
  with members:
    - "learning one hidden layer neural networks with landscape design"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "recovery guarantees for one hidden layer neural networks"
    - "electron proton dynamics in deep learning"
    - "learning non overlapping convolutional neural networks with multiple kernels"
    - "learning neural networks with two nonlinear layers in polynomial time"

Connected through hyperedge HE-2 (induced by source paper "on the learnability of deep random networks"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "convergence results for neural networks via electrodynamics"
    - "learning non overlapping convolutional neural networks with multiple kernels"
    - "learning neural networks with two nonlinear layers in polynomial time"
    - "when is a convolutional filter easy to learn"
    - "learning depth three neural networks in polynomial time"

Connected through hyperedge HE-3 (induced by source paper "towards a theoretical understanding of hashing based neural nets"):
  with members:
    - "on the convergence rate of training recurrent neural networks"
    - "adversarial examples from cryptographic pseudo random generators"
    - "spectral signatures in backdoor attacks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "the computational complexity of training relu s"
    - "adversarially robust generalization requires more data"
    - "a convergence theory for deep learning via over parameterization"
    - "relative error tensor low rank approximation"

Connected through hyperedge HE-4 (induced by source paper "improved learning of one hidden layer convolutional neural networks with overlaps"):
  with members:
    - "learning one convolutional layer with overlapping patches"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "learning one hidden layer neural networks with landscape design"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "learning relus via gradient descent"
    - "globally optimal gradient descent for a convnet with gaussian inputs"

Connected through hyperedge HE-5 (induced by source paper "escaping saddles with stochastic gradients"):
  with members:
    - "neon2 finding local minima via first order oracles"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "a generic approach for escaping saddle points"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "sub sampled cubic regularization for non convex optimization"
    - "on the gap between strict saddles and true convexity an omega log d lower bound for eigenvector approximation"
    - "how to escape saddle points efficiently"

Connected through hyperedge HE-6 (induced by source paper "a provably correct algorithm for deep learning that actually works"):
  with members:
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "recovery guarantees for one hidden layer neural networks"
    - "sgd learns the conjugate kernel class of the network"
    - "an analytical formula of population gradient for two layered relu network and its applications in convergence and critical point analysis"
    - "sgd learns over parameterized networks that provably generalize on linearly separable data"
    - "when is a convolutional filter easy to learn"
    - "deep learning and hierarchal generative models"
    - "convexified convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"):
  with members:
    - "learning one hidden layer neural networks with landscape design"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "learning relus via gradient descent"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "weight sharing is crucial to succesful optimization"
    - "recovery guarantees for one hidden layer neural networks"
    - "the loss surface of deep and wide neural networks"

Connected through hyperedge HE-8 (induced by source paper "when is a convolutional filter easy to learn"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "learning relus via gradient descent"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "weight sharing is crucial to succesful optimization"
    - "recovery guarantees for one hidden layer neural networks"
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"
    - "the loss surface of deep and wide neural networks"
    - "an analytical formula of population gradient for two layered relu network and its applications in convergence and critical point analysis"

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)