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: "on some provably correct cases of variational inference for topic models"
  Abstract: Variational inference is a very efficient and popular heuristic used in various forms in the context of latent variable models. It's closely related to Expectation Maximization (EM), and is applied when exact EM is computationally infeasible. Despite being immensely popular, current theoretical understanding of the effectiveness of variaitonal inference based algorithms is very limited. In this work we provide the first analysis of instances where variational inference algorithms converge to the global optimum, in the setting of topic models. #R##N#More specifically, we show that variational inference provably learns the optimal parameters of a topic model under natural assumptions on the topic-word matrix and the topic priors. The properties that the topic word matrix must satisfy in our setting are related to the topic expansion assumption introduced in (Anandkumar et al., 2013), as well as the anchor words assumption in (Arora et al., 2012c). The assumptions on the topic priors are related to the well known Dirichlet prior, introduced to the area of topic modeling by (Blei et al., 2003). #R##N#It is well known that initialization plays a crucial role in how well variational based algorithms perform in practice. The initializations that we use are fairly natural. One of them is similar to what is currently used in LDA-c, the most popular implementation of variational inference for topic models. The other one is an overlapping clustering algorithm, inspired by a work by (Arora et al., 2014) on dictionary learning, which is very simple and efficient. #R##N#While our primary goal is to provide insights into when variational inference might work in practice, the multiplicative, rather than the additive nature of the variational inference updates forces us to use fairly non-standard proof arguments, which we believe will be of general interest.

Connected through hyperedge HE-1 (induced by source paper "robust vertex enumeration for convex hulls in high dimensions"):
  with members:
    - "sparse approximation via generating point sets"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "a characterization theorem and an algorithm for a convex hull problem"
    - "a spectral algorithm for latent dirichlet allocation"
    - "learning topic models going beyond svd"
    - "exact recovery of sparsely used dictionaries"
    - "computing a nonnegative matrix factorization provably"

Connected through hyperedge HE-2 (induced by source paper "beyond log concavity provable guarantees for sampling multi modal distributions using simulated tempering langevin monte carlo"):
  with members:
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "provable learning of noisy or networks"
    - "simple efficient and neural algorithms for sparse coding"
    - "escaping the local minima via simulated annealing optimization of approximately convex functions"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "a spectral algorithm for latent dirichlet allocation"
    - "learning topic models going beyond svd"

Connected through hyperedge HE-3 (induced by source paper "linking sequences of events with sparse or no common occurrence across data sets"):
  with members:
    - "asymptotically optimal matching of multiple sequences to source distributions and training sequences"
    - "de anonymizing social networks"

Connected through hyperedge HE-4 (induced by source paper "recovery guarantee of non negative matrix factorization via alternating updates"):
  with members:
    - "simple efficient and neural algorithms for sparse coding"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "sparse topical coding"
    - "learning topic models going beyond svd"

Connected through hyperedge HE-5 (induced by source paper "necessary and sufficient conditions and a provably efficient algorithm for separable topic discovery"):
  with members:
    - "a topic modeling approach to ranking"
    - "a tensor approach to learning mixed membership community models"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "on the complexity of nonnegative matrix factorization"

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)