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: "on the optimization landscape of tensor decompositions"
  Abstract: Non-convex optimization with local search heuristics has been widely used in machine learning, achieving many state-of-art results. It becomes increasingly important to understand why they can work for these NP-hard problems on typical data. The landscape of many objective functions in learning has been conjectured to have the geometric property that "all local optima are (approximately) global optima", and thus they can be solved efficiently by local search algorithms. However, establishing such property can be very difficult. #R##N#In this paper, we analyze the optimization landscape of the random over-complete tensor decomposition problem, which has many applications in unsupervised learning, especially in learning latent variable models. In practice, it can be efficiently solved by gradient ascent on a non-convex objective. We show that for any small constant $\epsilon > 0$, among the set of points with function values $(1+\epsilon)$-factor larger than the expectation of the function, all the local maxima are approximate global maxima. Previously, the best-known result only characterizes the geometry in small neighborhoods around the true components. Our result implies that even with an initialization that is barely better than the random guess, the gradient ascent algorithm is guaranteed to solve this problem. #R##N#Our main technique uses Kac-Rice formula and random matrix theory. To our best knowledge, this is the first time when Kac-Rice formula is successfully applied to counting the number of local minima of a highly-structured random polynomial with dependent coefficients.

Connected through hyperedge HE-1 (induced by source paper "nonconvex low rank symmetric tensor completion from noisy data"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "iterative collaborative filtering for sparse noisy tensor estimation"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "fundamental conditions for low cp rank tensor completion"
    - "exact tensor completion with sum of squares"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "fast algorithms for robust pca via gradient descent"
    - "low rank tensor recovery via iterative hard thresholding"

Connected through hyperedge HE-2 (induced by source paper "analysis of the optimization landscapes for overcomplete representation learning"):
  with members:
    - "complete dictionary learning via 120001 4 norm maximization over the orthogonal group"
    - "on the global geometry of sphere constrained sparse blind deconvolution"
    - "global geometry of multichannel sparse blind deconvolution on the sphere"
    - "convolutional dictionary learning a comparative review and new algorithms"
    - "subgradient descent learns orthogonal dictionaries"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "how to escape saddle points efficiently"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"

Connected through hyperedge HE-3 (induced by source paper "understanding geometry of encoder decoder cnns"):
  with members:
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "a differential topological view of challenges in learning with feedforward neural networks"
    - "k space deep learning for accelerated mri"
    - "dnn or k nn that is the generalize vs memorize question"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"

Connected through hyperedge HE-4 (induced by source paper "who is afraid of big bad minima analysis of gradient flow in a spiked matrix tensor model"):
  with members:
    - "passed spurious descent algorithms and local minima in spiked matrix tensor models"
    - "marvels and pitfalls of the langevin algorithm in noisy high dimensional inference"
    - "depth creates no bad local minima"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "matrix completion has no spurious local minimum"
    - "information theoretically optimal sparse pca"
    - "a statistical model for tensor pca"

Connected through hyperedge HE-5 (induced by source paper "when does non orthogonal tensor decomposition have no spurious local minima"):
  with members:
    - "reinforcement learning of pomdp s using spectral methods"
    - "learning mixtures of gaussians in high dimensions"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "a tensor approach to learning mixed membership community models"
    - "tensor decompositions for learning latent variable models"
    - "most tensor problems are np hard"

Connected through hyperedge HE-6 (induced by source paper "are resnets provably better than linear predictors"):
  with members:
    - "introduction to online convex optimization"
    - "gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks"
    - "understanding the loss surface of neural networks for binary classification"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "learning one hidden layer neural networks with landscape design"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"
    - "how to escape saddle points efficiently"

Connected through hyperedge HE-7 (induced by source paper "towards the optimal construction of a loss function without spurious local minima for solving quadratic equations"):
  with members:
    - "the non convex geometry of low rank matrix optimization"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "phasemax convex phase retrieval via basis pursuit"
    - "matrix completion has no spurious local minimum"
    - "an elementary proof of convex phase retrieval in the natural parameter space via the linear program phasemax"
    - "a geometric analysis of phase retrieval"
    - "phase retrieval meets statistical learning theory a flexible convex relaxation"

Connected through hyperedge HE-8 (induced by source paper "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "on the global geometry of sphere constrained sparse blind deconvolution"
    - "fast robust and non convex subspace recovery"
    - "nonconvex matrix factorization from rank one measurements"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "phase retrieval via linear programming fundamental limits and algorithmic improvements"
    - "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"
    - "recovery guarantees for one hidden layer neural 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)