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 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.

Connected through hyperedge HE-1 (induced by source paper "meta learning for mixed linear regression"):
  with members:
    - "learning mixtures of linear regressions in subexponential time via fourier moments"
    - "sublinear optimal policy value estimation in contextual bandits"
    - "meta learning with implicit gradients"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "estimating learnability in the sublinear data regime"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning with latent embedding optimization"
    - "fast context adaptation via meta learning"

Connected through hyperedge HE-2 (induced by source paper "when can wasserstein gans minimize wasserstein distance"):
  with members:
    - "on gradient descent ascent for nonconvex concave minimax problems"
    - "sgd learns one layer networks in wgans"
    - "approximability of discriminators implies diversity in gans"
    - "spectral normalization for generative adversarial networks"
    - "negative momentum for improved game dynamics"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "the numerics of gans"

Connected through hyperedge HE-3 (induced by source paper "learning mixtures of linear regressions in subexponential time via fourier moments"):
  with members:
    - "list decodable linear regression"
    - "em converges for a mixture of many linear regressions"
    - "list decodable learning via sum of squares"
    - "nearly sample optimal sparse fourier transform in any dimension ripless and filterless"
    - "hyperplane clustering via dual principal component pursuit"
    - "sample efficient estimation and recovery in sparse fft via isolation on average"
    - "learning from untrusted data"
    - "fourier sparse interpolation without a frequency gap"

Connected through hyperedge HE-4 (induced by source paper "iterative least trimmed squares for mixed linear regression"):
  with members:
    - "high dimensional robust sparse regression"
    - "learning with bad training data via iterative trimmed loss minimization"
    - "efficient algorithms for outlier robust regression"
    - "sever a robust meta algorithm for stochastic optimization"
    - "learning mixtures of sparse linear regressions using sparse graph codes"
    - "being robust in high dimensions can be practical"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "solving a mixture of many random linear equations by tensor decomposition and alternating minimization"

Connected through hyperedge HE-5 (induced by source paper "em converges for a mixture of many linear regressions"):
  with members:
    - "on learning mixtures of well separated gaussians"
    - "solving a mixture of many random linear equations by tensor decomposition and alternating minimization"
    - "regularized em algorithms a unified framework and statistical guarantees"
    - "provable tensor methods for learning mixtures of generalized linear models"
    - "spectral experts for estimating mixtures of linear regressions"

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)