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: "dirichlet draws are sparse with high probability"
  Abstract: This note provides an elementary proof of the folklore fact that draws from a Dirichlet distribution (with parameters less than 1) are typically sparse (most coordinates are small).

Connected through hyperedge HE-1 (induced by source paper "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"):
  with members:
    - "concentrated differential privacy"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-2 (induced by source paper "on some provably correct cases of variational inference for topic models"):
  with members:
    - "simple efficient and neural algorithms for sparse coding"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "a two round variant of em for gaussian mixtures"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "learning topic models going beyond svd"
    - "computing a nonnegative matrix factorization provably"
    - "clustering with spectral norm and the k means algorithm"

Connected through hyperedge HE-3 (induced by source paper "a tensor approach to learning mixed membership community models"):
  with members:
    - "fast detection of overlapping communities via online tensor methods"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "breaking the small cluster barrier of graph clustering"
    - "a method of moments for mixture models and hidden markov models"
    - "a spectral algorithm for latent dirichlet allocation"
    - "statistical algorithms and a lower bound for detecting planted clique"
    - "low distortion inference of latent similarities from a multiplex social network"
    - "finding overlapping communities in social networks toward a rigorous approach"

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)