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 "bayesian experimental design using regularized determinantal point processes"):
  Abstract: In experimental design, we are given $n$ vectors in $d$ dimensions, and our goal is to select $k\ll n$ of them to perform expensive measurements, e.g., to obtain labels/responses, for a linear regression task. Many statistical criteria have been proposed for choosing the optimal design, with popular choices including A- and D-optimality. If prior knowledge is given, typically in the form of a $d\times d$ precision matrix $\mathbf A$, then all of the criteria can be extended to incorporate that information via a Bayesian framework. In this paper, we demonstrate a new fundamental connection between Bayesian experimental design and determinantal point processes, the latter being widely used for sampling diverse subsets of data. We use this connection to develop new efficient algorithms for finding $(1+\epsilon)$-approximations of optimal designs under four optimality criteria: A, C, D and V. Our algorithms can achieve this when the desired subset size $k$ is $\Omega(\frac{d_{\mathbf A}}{\epsilon} + \frac{\log 1/\epsilon}{\epsilon^2})$, where $d_{\mathbf A}\leq d$ is the $\mathbf A$-effective dimension, which can often be much smaller than $d$. Our results offer direct improvements over a number of prior works, for both Bayesian and classical experimental design, in terms of algorithm efficiency, approximation quality, and range of applicable criteria.

Includes members:
  Member "distributed estimation of the inverse hessian by determinantal averaging":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exact expressions for double descent and implicit regularization via surrogate random design")
  Member "minimax experimental design bridging the gap between statistical and worst case approaches to least squares regression":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improved guarantees and a multiple descent curve for the column subset selection problem and the nystr om method")
  Member "correcting the bias in least squares regression with volume rescaled sampling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exact expressions for double descent and implicit regularization via surrogate random design")
  Member "fast determinantal point processes via distortion free intermediate sampling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improved guarantees and a multiple descent curve for the column subset selection problem and the nystr om method")
  Member "proportional volume sampling and approximation algorithms for a optimal design":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a spectral approach to network design")
  Member "reverse iterative volume sampling for linear regression":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improved guarantees and a multiple descent curve for the column subset selection problem and the nystr om method")
  Member "leveraged volume sampling for linear regression":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "exact expressions for double descent and implicit regularization via surrogate random design")
  Member "lectures on randomized numerical linear algebra":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)