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: "tail bounds for volume sampled linear regression"
  Abstract: The $n \times d$ design matrix in a linear regression problem is given, but the response for each point is hidden unless explicitly requested. The goal is to observe only a small number $k \ll n$ of the responses, and then produce a weight vector whose sum of square loss over all points is at most $1+\epsilon$ times the minimum. A standard approach to this problem is to use i.i.d. leverage score sampling, but this approach is known to perform poorly when $k$ is small (e.g., $k = d$); in such cases, it is dominated by volume sampling, a joint sampling method that explicitly promotes diversity. How these methods compare for larger $k$ was not previously understood. #R##N#We prove that volume sampling can have poor behavior for large $k$ - indeed worse than leverage score sampling. We also show how to repair volume sampling using a new padding technique. We prove that padded volume sampling has at least as good a tail bound as leverage score sampling: sample size $k=O(d\log d + d/\epsilon)$ suffices to guarantee total loss at most $1+\epsilon$ times the minimum with high probability. The main technical challenge is proving tail bounds for the sums of dependent random matrices that arise from volume sampling.

Connected through hyperedge HE-1 (induced by source paper "active regression via linear sample sparsification"):
  with members:
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "relative error tensor low rank approximation"
    - "unbiased estimates for linear regression via volume sampling"
    - "fourier sparse interpolation without a frequency gap"
    - "convergence rates of active learning for maximum likelihood estimation"
    - "constructing linear sized spectral sparsification in almost linear time"
    - "sketching as a tool for numerical linear algebra"
    - "near optimal coresets for least squares regression"

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)