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 "improved guarantees and a multiple descent curve for the column subset selection problem and the nystr om method"):
  Abstract: The Column Subset Selection Problem (CSSP) and the Nystrom method are among the leading tools for constructing small low-rank approximations of large datasets in machine learning and scientific computing. A fundamental question in this area is: how well can a data subset of size k compete with the best rank k approximation? We develop techniques which exploit spectral properties of the data matrix to obtain improved approximation guarantees which go beyond the standard worst-case analysis. Our approach leads to significantly better bounds for datasets with known rates of singular value decay, e.g., polynomial or exponential decay. Our analysis also reveals an intriguing phenomenon: the approximation factor as a function of k may exhibit multiple peaks and valleys, which we call a multiple-descent curve. A lower bound we establish shows that this behavior is not an artifact of our analysis, but rather it is an inherent property of the CSSP and Nystrom tasks. Finally, using the example of a radial basis function (RBF) kernel, we show that both our improved bounds and the multiple-descent curve can be observed on real datasets simply by varying the RBF parameter.

Includes members:
  Member "two models of double descent for weak features":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a deep conditioning treatment of neural networks")
  Member "exact sampling of determinantal point processes with sublinear time preprocessing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive sampling for stochastic risk averse learning")
  Member "bayesian experimental design using regularized determinantal point processes":
    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 "exact expressions for double descent and implicit regularization via surrogate random design":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal regularization can mitigate double descent")
  Member "minimax experimental design bridging the gap between statistical and worst case approaches to least squares regression":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "exact expressions for double descent and implicit regularization via surrogate random design")
  Member "reverse iterative volume sampling for linear regression":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep batch active learning by diverse uncertain gradient lower bounds")
  Member "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "diversity and inclusion metrics for subset selection")

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)