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 "sparse regression via range counting"):
  Abstract: The sparse regression problem, also known as best subset selection problem, can be cast as follows: Given a real $d \times n$ matrix $A$, a vector $y$ in $\mathbb{R}^d$, and an integer $2 \leq k \leq d$, find an affine combination of at most $k$ columns of $A$ that is closest to $y$. We describe a $O(n^{k-1} \log^{d-k+2} n)$-time randomized $(1+\varepsilon)$-approximation algorithm for this problem with $d$ and $\varepsilon$ constant. This is the first algorithm for this problem running in time $o(n^k)$. Its running time is similar to the query time of a data structure recently proposed by Har-Peled, Indyk, and Mahabadi (ICALP'18), while not requiring any preprocessing. Up to polylogarithmic factors, it matches a conditional lower bound relying on a conjecture about affine degeneracy testing. In the special case where $k = d = O(1)$, we also provide a simple $O(n^{d-1+\epsilon})$-time deterministic exact algorithm. Finally, we show how to adapt the approximation algorithm for the sparse linear regression and sparse convex regression problems with the same running time, up to polylogarithmic factors.

Includes members:
  Member "approximate sparse linear regression":
    also connected through other hyperedges:
      (none)
  Member "online sparse linear regression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adaptive feature selection computationally efficient online sparse linear regression under rip")
  Member "variable selection is hard":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximate guarantees for dictionary learning")
  Member "orthogonal range searching on the ram revisited":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "path query data structures in practice")

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)