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: "provable tensor methods for learning mixtures of generalized linear models"
  Abstract: We consider the problem of learning mixtures of generalized linear models (GLM) which arise in classification and regression problems. Typical learning approaches such as expectation maximization (EM) or variational Bayes can get stuck in spurious local optima. In contrast, we present a tensor decomposition method which is guaranteed to correctly recover the parameters. The key insight is to employ certain feature transformations of the input, which depend on the input generative model. Specifically, we employ score function tensors of the input and compute their cross-correlation with the response variable. We establish that the decomposition of this tensor consistently recovers the parameters, under mild non-degeneracy conditions. We demonstrate that the computational and sample complexity of our method is a low order polynomial of the input and the latent dimensions.

Connected through hyperedge HE-1 (induced by source paper "meta learning for mixed linear regression"):
  with members:
    - "learning mixtures of linear regressions in subexponential time via fourier moments"
    - "sublinear optimal policy value estimation in contextual bandits"
    - "meta learning with implicit gradients"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "estimating learnability in the sublinear data regime"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "learning mixtures of linear regressions with nearly optimal complexity"
    - "meta learning with latent embedding optimization"

Connected through hyperedge HE-2 (induced by source paper "learning in gated neural networks"):
  with members:
    - "can sgd learn recurrent neural networks with provable generalization"
    - "non asymptotic identification of lti systems from a single trajectory"
    - "robust spectral filtering and anomaly detection"
    - "towards binary valued gates for robust lstm training"
    - "learning one hidden layer neural networks under general input distributions"
    - "breaking the gridlock in mixture of experts consistent and efficient algorithms"
    - "on the convergence rate of training recurrent neural networks"
    - "learning without mixing towards a sharp analysis of linear system identification"

Connected through hyperedge HE-3 (induced by source paper "learning mixtures of linear regressions in subexponential time via fourier moments"):
  with members:
    - "list decodable linear regression"
    - "em converges for a mixture of many linear regressions"
    - "list decodable learning via sum of squares"
    - "nearly sample optimal sparse fourier transform in any dimension ripless and filterless"
    - "learning mixtures of linear regressions with nearly optimal complexity"
    - "hyperplane clustering via dual principal component pursuit"
    - "sample efficient estimation and recovery in sparse fft via isolation on average"
    - "learning from untrusted data"

Connected through hyperedge HE-4 (induced by source paper "iterative least trimmed squares for mixed linear regression"):
  with members:
    - "high dimensional robust sparse regression"
    - "learning mixtures of linear regressions with nearly optimal complexity"
    - "learning with bad training data via iterative trimmed loss minimization"
    - "efficient algorithms for outlier robust regression"
    - "sever a robust meta algorithm for stochastic optimization"
    - "learning mixtures of sparse linear regressions using sparse graph codes"
    - "being robust in high dimensions can be practical"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"

Connected through hyperedge HE-5 (induced by source paper "em converges for a mixture of many linear regressions"):
  with members:
    - "learning mixtures of linear regressions with nearly optimal complexity"
    - "on learning mixtures of well separated gaussians"
    - "solving a mixture of many random linear equations by tensor decomposition and alternating minimization"
    - "regularized em algorithms a unified framework and statistical guarantees"
    - "spectral experts for estimating mixtures of linear regressions"

Connected through hyperedge HE-6 (induced by source paper "breaking the gridlock in mixture of experts consistent and efficient algorithms"):
  with members:
    - "deep mixture of experts via shallow embedding"
    - "tafe net task aware feature embeddings for efficient learning and inference"
    - "learning one hidden layer neural networks with landscape design"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "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"
    - "attention is all you need"
    - "orthogonalized als a theoretically principled tensor decomposition algorithm for practical use"

Connected through hyperedge HE-7 (induced by source paper "gradient descent for one hidden layer neural networks polynomial convergence and sq lower bounds"):
  with members:
    - "learning one convolutional layer with overlapping patches"
    - "learning two layer neural networks with symmetric inputs"
    - "on the complexity of learning neural networks"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "sgd learns the conjugate kernel class of the network"
    - "learning depth three neural networks in polynomial time"
    - "convergence analysis of two layer neural networks with relu activation"
    - "reliably learning the relu in polynomial time"

Connected through hyperedge HE-8 (induced by source paper "complete dictionary recovery over the sphere i overview and the geometric picture"):
  with members:
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "polynomial time tensor decompositions with sum of squares"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"
    - "a geometric analysis of phase retrieval"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"
    - "simple efficient and neural algorithms for sparse coding"

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)