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 algorithms for inference in topic models"):
  Abstract: Recently, there has been considerable progress on designing algorithms with provable guarantees -- typically using linear algebraic methods -- for parameter learning in latent variable models. But designing provable algorithms for inference has proven to be more challenging. Here we take a first step towards provable inference in topic models. We leverage a property of topic models that enables us to construct simple linear estimators for the unknown topic proportions that have small variance, and consequently can work with short documents. Our estimators also correspond to finding an estimate around which the posterior is well-concentrated. We show lower bounds that for shorter documents it can be information theoretically impossible to find the hidden topics. Finally, we give empirical results that demonstrate that our algorithm works on realistic topic models. It yields good solutions on synthetic data and runs in time comparable to a {\em single} iteration of Gibbs sampling.

Includes members:
  Member "beating the perils of non convexity guaranteed training of neural networks using tensor methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "no spurious local minima in deep quadratic networks")
  Member "learning mixtures of gaussians in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "monotone probability distributions over the boolean cube can be learned with sublinear samples")
  Member "a tensor approach to learning mixed membership community models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "finding a latent k simplex in o k nnz data time via subset smoothing")
  Member "provable bounds for learning some deep representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "span recovery for deep neural networks with applications to input obfuscation")
  Member "new algorithms for learning incoherent and overcomplete dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "when can wasserstein gans minimize wasserstein distance")
  Member "a polynomial time algorithm for lossy population recovery":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient average case population recovery in the presence of insertions and deletions")
  Member "a practical algorithm for topic modeling with provable guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "leveraging just a few keywords for fine grained aspect detection through weakly supervised co training")
  Member "learning mixtures of spherical gaussians moment methods and spectral decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "em algorithm is sample optimal for learning mixtures of well separated gaussians")

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)