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: "faster algorithms for high dimensional robust covariance estimation"
  Abstract: We study the problem of estimating the covariance matrix of a high-dimensional distribution when a small constant fraction of the samples can be arbitrarily corrupted. Recent work gave the first polynomial time algorithms for this problem with near-optimal error guarantees for several natural structured distributions. Our main contribution is to develop faster algorithms for this problem whose running time nearly matches that of computing the empirical covariance. #R##N#Given $N = \tilde{\Omega}(d^2/\epsilon^2)$ samples from a $d$-dimensional Gaussian distribution, an $\epsilon$-fraction of which may be arbitrarily corrupted, our algorithm runs in time $\tilde{O}(d^{3.26})/\mathrm{poly}(\epsilon)$ and approximates the unknown covariance matrix to optimal error up to a logarithmic factor. Previous robust algorithms with comparable error guarantees all have runtimes $\tilde{\Omega}(d^{2 \omega})$ when $\epsilon = \Omega(1)$, where $\omega$ is the exponent of matrix multiplication. We also provide evidence that improving the running time of our algorithm may require new algorithmic techniques.

Connected through hyperedge HE-1 (induced by source paper "robust mean estimation under coordinate level corruption"):
  with members:
    - "holodetect few shot learning for error detection"
    - "how hard is robust mean estimation"
    - "efficient algorithms and lower bounds for robust linear regression"
    - "efficient algorithms for outlier robust regression"
    - "sever a robust meta algorithm for stochastic optimization"
    - "being robust in high dimensions can be practical"
    - "holoclean holistic data repairs with probabilistic inference"
    - "learning from untrusted data"

Connected through hyperedge HE-2 (induced by source paper "recent advances in algorithmic high dimensional robust statistics"):
  with members:
    - "list decodable linear regression"
    - "high dimensional robust estimation of sparse models via trimmed hard thresholding"
    - "average case lower bounds for learning sparse mixtures robust estimation and semirandom adversaries"
    - "how hard is robust mean estimation"
    - "list decodable learning via sum of squares"
    - "quantum entropy scoring for fast robust mean estimation and improved outlier detection"
    - "high dimensional estimation via sum of squares proofs"
    - "high dimensional robust sparse regression"

Connected through hyperedge HE-3 (induced by source paper "quantum entropy scoring for fast robust mean estimation and improved outlier detection"):
  with members:
    - "how hard is robust mean estimation"
    - "high dimensional robust mean estimation in nearly linear time"
    - "being robust in high dimensions can be practical"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "agnostic estimation of mean and covariance"
    - "robust estimators in high dimensions without the computational intractability"
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"
    - "practical and optimal lsh for angular distance"

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)