human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "discrepancy coresets and sketches in machine learning"):
  with members:
    - "on coresets for logistic regression"
    - "improved coresets for kernel density estimates"
    - "epsilon approximations and epsilon nets"
    - "optimal quantile approximation in streams"
    - "new frameworks for offline and streaming coreset constructions"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-2 (induced by source paper "coresets for minimum enclosing balls over sliding windows"):
  with members:
    - "efficient representative subset selection over sliding windows"
    - "real time influence maximization on dynamic social streams"
    - "coresets for kernel regression"
    - "submodular optimization over sliding windows"
    - "streamed learning one pass svms"

Question: Please tell me which class the center node 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)