human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning balanced mixtures of discrete distributions with small sample"):
  Abstract: We study the problem of partitioning a small sample of n individuals from a mixture of k product distributions over a Boolean cube {0, 1} K according to their distributions. Each distribution is des cribed by a vector of allele frequencies in R K . Given two distributions, we use γ to denote the average l 2 distance in frequencies across K dimensions, which measures the statistical divergence between them. We study the case assuming that bits are independently distributed acro ss K dimensions. This work demonstrates that, for a balanced input instance for k = 2, a certain graph-based optimization function returns the correct partition with high probability, where a weighted graph G is formed over n individuals, whose pairwise hamming distances between their corresponding bit vectors define th e edge weights, so long as K =  (ln n/γ ) and K n = ˜  ln n/γ 2 � . The function computes a maximum-weight balanced cut of G, where the weight of a cut is the sum of the weights across all edges in the cut. This r esult demonstrates a nice property in the high-dimensional feature space: one can trade off the number of features that are required with the size of the sample to accomplish certain tasks like clustering.

Includes members:
  Member "a two round variant of em for gaussian mixtures":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "provably efficient exploration for rl with unsupervised learning")
  Member "learning nonsingular phylogenies and hidden markov models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "guaranteed scalable learning of latent tree models")

Question: Please tell me which class the source paper 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)