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: "learning mixtures of product distributions via higher multilinear moments"
  Abstract: Learning mixtures of $k$ binary product distributions is a central problem in computational learning theory, but one where there are wide gaps between the best known algorithms and lower bounds (even for restricted families of algorithms). We narrow many of these gaps by developing novel insights about how to reason about higher order multilinear moments. Our results include: #R##N#1) An $n^{O(k^2)}$ time algorithm for learning mixtures of binary product distributions, giving the first improvement on the $n^{O(k^3)}$ time algorithm of Feldman, O'Donnell and Servedio #R##N#2) An $n^{\Omega(\sqrt{k})}$ statistical query lower bound, improving on the $n^{\Omega(\log k)}$ lower bound that is based on connections to sparse parity with noise #R##N#3) An $n^{O(\log k)}$ time algorithm for learning mixtures of $k$ subcubes. This special case can still simulate many other hard learning problems, but is much richer than any of them alone. As a corollary, we obtain more flexible algorithms for learning decision trees under the uniform distribution, that work with stochastic transitions, when we are only given positive examples and with a polylogarithmic number of samples for any fixed $k$. #R##N#Our algorithms are based on a win-win analysis where we either build a basis for the moments or locate a degeneracy that can be used to simplify the problem, which we believe will have applications to other learning problems over discrete domains.

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)