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 "proportional volume sampling and approximation algorithms for a optimal design"):
  Abstract: We study the optimal design problems where the goal is to choose a set of linear measurements to obtain the most accurate estimate of an unknown vector in $d$ dimensions. We study the $A$-optimal design variant where the objective is to minimize the average variance of the error in the maximum likelihood estimate of the vector being measured. The problem also finds applications in sensor placement in wireless networks, sparse least squares regression, feature selection for $k$-means clustering, and matrix approximation. In this paper, we introduce proportional volume sampling to obtain improved approximation algorithms for $A$-optimal design. Our main result is to obtain improved approximation algorithms for the $A$-optimal design problem by introducing the proportional volume sampling algorithm. Our results nearly optimal bounds in the asymptotic regime when the number of measurements done, $k$, is significantly more than the dimension $d$. We also give first approximation algorithms when $k$ is small including when $k=d$. The proportional volume-sampling algorithm also gives approximation algorithms for other optimal design objectives such as $D$-optimal design and generalized ratio objective matching or improving previous best known results. Interestingly, we show that a similar guarantee cannot be obtained for the $E$-optimal design problem. We also show that the $A$-optimal design problem is NP-hard to approximate within a fixed constant when $k=d$.

Includes members:
  Member "unbiased estimates for linear regression via volume sampling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "blinkml efficient maximum likelihood estimation with probabilistic guarantees")
  Member "subsampling for ridge regression via regularized volume sampling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exact expressions for double descent and implicit regularization via surrogate random design")
  Member "real stable polynomials and matroids optimization and counting":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dynamic sampling from graphical models")
  Member "on largest volume simplices and sub determinants":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "maximizing the number of spanning trees in a connected graph")
  Member "randomized rounding for the largest simplex problem":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "batch active learning using determinantal point processes")
  Member "deterministic feature selection for k means clustering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "subset selection for matrices with fixed blocks")
  Member "efficient volume sampling for row column subset selection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improved guarantees and a multiple descent curve for the column subset selection problem and the nystr om method")

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)