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 "fast subspace approximation via greedy least squares"):
  Abstract: In this note, we develop fast and deterministic dimensionality reduction techniques for a family of subspace approximation problems. Let $P\subset \mathbbm{R}^N$ be a given set of $M$ points. The techniques developed herein find an $O(n \log M)$-dimensional subspace that is guaranteed to always contain a near-best fit $n$-dimensional hyperplane $\mathcal{H}$ for $P$ with respect to the cumulative projection error $(\sum_{{\bf x} \in P} \| {\bf x} - \Pi_\mathcal{H} {\bf x} \|^p_2)^{1/p}$, for any chosen $p > 2$. The deterministic algorithm runs in $\tilde{O} (MN^2)$-time, and can be randomized to run in only $\tilde{O} (MNn)$-time while maintaining its error guarantees with high probability. In the case $p = \infty$ the dimensionality reduction techniques can be combined with efficient algorithms for computing the John ellipsoid of a data set in order to produce an $n$-dimensional subspace whose maximum $\ell_2$-distance to any point in the convex hull of $P$ is minimized. The resulting algorithm remains $\tilde{O} (MNn)$-time. In addition, the dimensionality reduction techniques developed herein can also be combined with other existing subspace approximation algorithms for $2 < p \leq \infty$ - including more accurate algorithms based on convex programming relaxations - in order to reduce their runtimes.

Includes members:
  Member "a unified framework for approximating and clustering data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "algorithms and hardness for subspace approximation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "turning big data into tiny data constant size coresets for k means pca and projective clustering")

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.CG(Computational Geometry)