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 classifiers"
  Abstract: We consider the problem of learning associative mixtures for classification and regression problems, where the output is modeled as a mixture of conditional distributions, conditioned on the input. In contrast to approaches such as expectation maximization (EM) or variational Bayes, which can get stuck in bad local optima, we present a tensor decomposition method which is guaranteed to correctly recover the parameters. The key insight is to learn score function features of the input, and employ them in a moment-based approach for learning associative mixtures. Specifically, we construct the cross-moment tensor between the label and higher order score functions of the input. We establish that the decomposition of this tensor consistently recovers the components of the associative mixture under some simple non-degeneracy assumptions. Thus, we establish that feature learning is the critical ingredient for consistent estimation of associative mixtures using tensor decomposition approaches.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "a geometric analysis of phase retrieval"):
  with members:
    - "non convex phase retrieval from stft measurements"
    - "phasemax convex phase retrieval via basis pursuit"
    - "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"
    - "efficient approaches for escaping higher order saddle points in non convex optimization"
    - "phase retrieval via incremental truncated wirtinger flow"
    - "phaseless rcovery using gauss newton method"

Connected through hyperedge HE-4 (induced by source paper "score function features for discriminative learning matrix and tensor framework"):
  with members:
    - "sample complexity analysis for learning overcomplete latent variable models through tensor methods"
    - "how transferable are features in deep neural networks"
    - "provable methods for training neural networks with sparse connectivity"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "provable bounds for learning some deep representations"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "new algorithms for learning incoherent and overcomplete dictionaries"

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)