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 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.

Connected through hyperedge HE-1 (induced by source paper "learning networked exponential families with network lasso"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "localized linear regression in networked data"
    - "collaborative filtering bandits"
    - "network lasso clustering and optimization in large graphs"
    - "online clustering of bandits"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "a new evaluation framework for topic modeling algorithms based on synthetic corpora"):
  with members:
    - "integrating document clustering and topic modeling"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "beyond word frequency bursts lulls and scaling in the temporal distributions of words"

Connected through hyperedge HE-3 (induced by source paper "prior aware dual decomposition document specific topic inference for spectral topic models"):
  with members:
    - "spectral methods for correlated topic models"
    - "robust spectral inference for joint stochastic matrix factorization"
    - "probabilistic latent semantic analysis"
    - "when are overcomplete topic models identifiable uniqueness of tensor tucker decompositions with structured sparsity"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "on smoothing and inference for topic models"
    - "a tutorial on dual decomposition and lagrangian relaxation for inference in natural language processing"
    - "a spectral algorithm for latent dirichlet allocation"

Connected through hyperedge HE-4 (induced by source paper "provable benefits of representation learning"):
  with members:
    - "a non generative framework and convex relaxations for unsupervised learning"
    - "from word embeddings to item recommendation"
    - "a latent variable model approach to pmi based word embeddings"
    - "distributed representations of words and phrases and their compositionality"
    - "a spectral algorithm for latent dirichlet allocation"

Connected through hyperedge HE-5 (induced by source paper "provable alternating gradient descent for non negative matrix factorization with strong correlations"):
  with members:
    - "a practical algorithm for topic modeling with provable guarantees"
    - "learning topic models going beyond svd"
    - "computing a nonnegative matrix factorization provably"

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)