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: "on robustness of principal component regression"
  Abstract: Consider the setting of Linear Regression where the observed response variables, in expectation, are linear functions of the $p$-dimensional covariates. To achieve vanishing prediction error, the number of required samples scales faster than $p \sigma^2$, where $\sigma^2$ is a bound on the noise variance. In high-dimensional settings where $p$ is large but the covariates admit a low-dimensional representation (say $r \ll p$), Principal Component Regression (PCR) is an effective approach; here, the response variables are regressed with respect to the principal components of the covariates. The resulting number of required samples to achieve vanishing prediction error now scales faster than $r \sigma^2 (\ll p \sigma^2)$. Despite the tremendous utility of PCR, its ability to handle settings with noisy, missing, and mixed (discrete and continuous) valued covariates is not understood and remains an important open challenge. As our main contribution, we address this challenge by rigorously establishing that PCR is robust to noisy, sparse, and possibly mixed valued covariates. Specifically, under PCR, vanishing prediction error is achieved if the number of samples scales as $r \max(\sigma^2, \rho^{-4} \log^5(p))$, where $\rho$ denotes the fraction of observed (noisy) covariates. We establish generalization error bounds for PCR, which provides a systematic approach to select the number of components $r$ in a data-driven manner. The key to our result is a simple, but powerful equivalence between: (i) PCR; (ii) Linear Regression with covariate pre-processing via Hard Singular Value Thresholding (HSVT). From a technical standpoint, this work advances the state-of-the-art analysis for HSVT by establishing stronger guarantees with respect to the $\| \cdot \|_{2, \infty}$-error for the estimated matrix rather than the Frobenius norm as is standard in the matrix estimation/completion literature.

Connected through hyperedge HE-1 (induced by source paper "tspdb time series predict db"):
  with members:
    - "model agnostic time series analysis via matrix estimation"
    - "bayesdb a probabilistic programming system for querying the probable implications of data"

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)