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 mixtures of linear regressions with nearly optimal complexity"):
  Abstract: Mixtures of Linear Regressions (MLR) is an important mixture model with many applications. In this model, each observation is generated from one of the several unknown linear regression components, where the identity of the generated component is also unknown. Previous works either assume strong assumptions on the data distribution or have high complexity. This paper proposes a fixed parameter tractable algorithm for the problem under general conditions, which achieves global convergence and the sample complexity scales nearly linearly in the dimension. In particular, different from previous works that require the data to be from the standard Gaussian, the algorithm allows the data from Gaussians with different covariances. When the conditional number of the covariances and the number of components are fixed, the algorithm has nearly optimal sample complexity $N = \tilde{O}(d)$ as well as nearly optimal computational complexity $\tilde{O}(Nd)$, where $d$ is the dimension of the data space. To the best of our knowledge, this approach provides the first such recovery guarantee for this general setting.

Includes members:
  Member "sample efficient learning of mixtures":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "em algorithm is sample optimal for learning mixtures of well separated gaussians")
  Member "convergence analysis of two layer neural networks with relu activation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "lazysvd even faster svd decomposition yet without agonizing pain":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "solving a mixture of many random linear equations by tensor decomposition and alternating minimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "meta learning for mixed linear regression")
  Member "learning mixtures of gaussians in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "monotone probability distributions over the boolean cube can be learned with sublinear samples")
  Member "spectral experts for estimating mixtures of linear regressions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "meta learning for mixed linear regression")
  Member "settling the polynomial learnability of mixtures of gaussians":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "when can wasserstein gans minimize wasserstein distance")

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)