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 "iterative least trimmed squares for mixed linear regression"):
  Abstract: Given a linear regression setting, Iterative Least Trimmed Squares (ILTS) involves alternating between (a) selecting the subset of samples with lowest current loss, and (b) re-fitting the linear model only on that subset. Both steps are very fast and simple. In this paper we analyze ILTS in the setting of mixed linear regression with corruptions (MLR-C). We first establish deterministic conditions (on the features etc.) under which the ILTS iterate converges linearly to the closest mixture component. We also provide a global algorithm that uses ILTS as a subroutine, to fully solve mixed linear regressions with corruptions. We then evaluate it for the widely studied setting of isotropic Gaussian features, and establish that we match or better existing results in terms of sample complexity. Finally, we provide an ODE analysis for a gradient-descent variant of ILTS that has optimal time complexity. #R##N#Our results provide initial theoretical evidence that iteratively fitting to the best subset of samples -- a potentially widely applicable idea -- can provably provide state of the art performance in bad training data settings.

Includes members:
  Member "high dimensional robust sparse regression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks")
  Member "learning mixtures of linear regressions with nearly optimal complexity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "meta learning for mixed linear regression")
  Member "learning with bad training data via iterative trimmed loss minimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dividemix learning with noisy labels as semi supervised learning")
  Member "efficient algorithms for outlier robust regression":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "certified robustness to label flipping attacks via randomized smoothing")
  Member "sever a robust meta algorithm for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the effectiveness of mitigating data poisoning attacks with gradient shaping")
  Member "learning mixtures of sparse linear regressions using sparse graph codes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust federated learning in a heterogeneous environment")
  Member "being robust in high dimensions can be practical":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "private mean estimation of heavy tailed distributions")
  Member "list decodable robust mean estimation and learning mixtures of spherical gaussians":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "meta learning for mixed linear regression")

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)