human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "sample complexity analysis for learning overcomplete latent variable models through tensor methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beating the perils of non convexity guaranteed training of neural networks using tensor methods")
  Member "learning mixtures of ranking models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "supporting hard queries over probabilistic preferences")
  Member "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multiresolution tensor learning for efficient and interpretable spatial analysis")
  Member "score function features for discriminative learning matrix and tensor framework":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning in gated neural networks")
  Member "fast detection of overlapping communities via online tensor methods":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "guaranteed scalable learning of latent tree models")
  Member "discriminative features via generalized eigenvectors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis")
  Member "learning mixtures of linear classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "breaking the gridlock in mixture of experts consistent and efficient algorithms")
  Member "nonparametric estimation of multi view latent variable models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "spectral learning of binomial hmms for dna methylation data")

Question: Please tell me which class the source paper 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)