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 "near optimal coresets of kernel density estimates"):
  Abstract: We construct near-optimal coresets for kernel density estimate for points in $\mathbb{R^d}$ when the kernel is positive definite. Specifically we show a polynomial time construction for a coreset of size $O(\sqrt{d\log (1/\epsilon)}/\epsilon)$, and we show a near-matching lower bound of size $\Omega(\sqrt{d}/\epsilon)$. The upper bound is a polynomial in $1/\epsilon$ improvement when $d \in [3,1/\epsilon^2)$ (for all kernels except the Gaussian kernel which had a previous upper bound of $O((1/\epsilon) \log^d (1/\epsilon))$) and the lower bound is the first known lower bound to depend on $d$ for this problem. Moreover, the upper bound restriction that the kernel is positive definite is significant in that it applies to a wide-variety of kernels, specifically those most important for machine learning. This includes kernels for information distances and the sinc kernel which can be negative.

Includes members:
  Member "improved coresets for kernel density estimates":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "finding the mode of a kernel density estimate")
  Member "the gram schmidt walk a cure for the banaszczyk blues":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a spectral approach to network design")
  Member "geometric inference on kernel density estimates":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "finding the mode of a kernel density estimate")
  Member "on the equivalence between herding and conditional gradient algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the equivalence between kernel quadrature rules and random feature expansions")
  Member "a gentle introduction to the kernel distance":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the gaussiansketch for almost relative error kernel distance")
  Member "algorithms for eps approximations of terrains":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "epsilon approximations and epsilon nets")

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)