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 "a unified framework for linear dimensionality reduction in l1"):
  Abstract: For a family of interpolation norms $\| \cdot \|_{1,2,s}$ on $\mathbb{R}^n$, we provide a distribution over random matrices $\Phi_s \in \mathbb{R}^{m \times n}$ parametrized by sparsity level $s$ such that for a fixed set $X$ of $K$ points in $\mathbb{R}^n$, if $m \geq C s \log(K)$ then with high probability, $\frac{1}{2} \| x \|_{1,2,s} \leq \| \Phi_s (x) \|_1 \leq 2 \| x\|_{1,2,s}$ for all $x\in X$. Several existing results in the literature reduce to special cases of this result at different values of $s$: for $s=n$, $\| x\|_{1,2,n} \equiv \| x \|_{1}$ and we recover that dimension reducing linear maps can preserve the $\ell_1$-norm up to a distortion proportional to the dimension reduction factor, which is known to be the best possible such result. For $s=1$, $\|x \|_{1,2,1} \equiv \| x \|_{2}$, and we recover an $\ell_2 / \ell_1$ variant of the Johnson-Lindenstrauss Lemma for Gaussian random matrices. Finally, if $x$ is $s$-sparse, then $\| x \|_{1,2,s} = \| x \|_1$ and we recover that $s$-sparse vectors in $\ell_1^n$ embed into $\ell_1^{\mathcal{O}(s \log(n))}$ via sparse random matrix constructions.

Includes members:
  Member "one bit compressed sensing by linear programming":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "new and improved johnson lindenstrauss embeddings via the restricted isometry property":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "finding the mode of a kernel density estimate")
  Member "finite volume spaces and sparsification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sparse sums of positive semidefinite matrices")
  Member "new bounds for restricted isometry constants":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the use of mutual coherence to prove ell 1 ell 0 equivalence in classification problems")
  Member "combining geometry and combinatorics a unified approach to sparse signal recovery":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sublinear time non adaptive group testing with o k log n tests via bit mixing coding")

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.DS(Data Structures and Algorithms)