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 "provable approximations for constrained ell_p regression"):
  Abstract: The $\ell_p$ linear regression problem is to minimize $f(x)=||Ax-b||_p$ over $x\in\mathbb{R}^d$, where $A\in\mathbb{R}^{n\times d}$, $b\in \mathbb{R}^n$, and $p>0$. To avoid overfitting and bound $||x||_2$, the constrained $\ell_p$ regression minimizes $f(x)$ over every unit vector $x\in\mathbb{R}^d$. This makes the problem non-convex even for the simplest case $d=p=2$. Instead, ridge regression is used to minimize the Lagrange form $f(x)+\lambda ||x||_2$ over $x\in\mathbb{R}^d$, which yields a convex problem in the price of calibrating the regularization parameter $\lambda>0$. We provide the first provable constant factor approximation algorithm that solves the constrained $\ell_p$ regression directly, for every constant $p,d\geq 1$. Using core-sets, its running time is $O(n \log n)$ including extensions for streaming and distributed (big) data. In polynomial time, it can handle outliers, $p\in (0,1)$ and minimize $f(x)$ over every $x$ and permutation of rows in $A$. Experimental results are also provided, including open source and comparison to existing software.

Includes members:
  Member "minimizing sum of non convex but piecewise log lipschitz functions using coresets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "coresets for gaussian mixture models of any shape")
  Member "l1 norm error function robustness and outlier regularization":
    also connected through other hyperedges:
      (none)
  Member "new frameworks for offline and streaming coreset constructions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sets clustering")
  Member "on the sensitivity of shape fitting problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "coresets for clustering in graphs of bounded treewidth")
  Member "a unified framework for approximating and clustering data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximating spectral clustering via sampling a review")

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)