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: "pac learning mixtures of axis aligned gaussians with no separation assumption"
  Abstract: We propose and analyze a new vantage point for the learning of mixtures of Gaussians: namely, the PAC-style model of learning probability distributions introduced by Kearns et al. [13]. Here the task is to construct a hypothesis mixture of Gaussians that is statistically indistinguishable from the actual mixture generating the data; specifically, the KL divergence should be at most ǫ. In this scenario, we give a poly(n/ǫ) time algorithm that learns the class of mixtures of any constant number of axis-aligned Gaussians in R n . Our algorithm makes no assumptions about the separation between the means of the Gaussians, nor does it have any dependence on the minimum mixing weight. This is in contrast to learning results known in the “clustering” model, where such assumptions are unavoidable. Our algorithm relies on the method of moments, and a subalgorithm developed in [8] for a discrete mixture-learning problem.

Connected through hyperedge HE-1 (induced by source paper "learning arbitrary statistical mixtures of discrete distributions"):
  with members:
    - "probabilistic latent semantic analysis"
    - "learning mixtures of arbitrary distributions over large discrete domains"
    - "a method of moments for mixture models and hidden markov models"
    - "a spectral algorithm for latent dirichlet allocation"
    - "learning topic models going beyond svd"
    - "learning k modal distributions via testing"
    - "settling the polynomial learnability of mixtures of gaussians"
    - "polynomial learning of distribution families"

Connected through hyperedge HE-2 (induced by source paper "learning mixtures of arbitrary distributions over large discrete domains"):
  with members:
    - "probabilistic latent semantic analysis"
    - "a method of moments for mixture models and hidden markov models"
    - "learning topic models going beyond svd"
    - "learning k modal distributions via testing"
    - "settling the polynomial learnability of mixtures of gaussians"
    - "polynomial learning of distribution families"
    - "learning nonsingular phylogenies and hidden markov models"

Connected through hyperedge HE-3 (induced by source paper "a method of moments for mixture models and hidden markov models"):
  with members:
    - "settling the polynomial learnability of mixtures of gaussians"
    - "polynomial learning of distribution families"
    - "a simpler approach to matrix completion"
    - "a spectral algorithm for learning hidden markov models"
    - "learning nonsingular phylogenies and hidden markov models"

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)