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 "vbald variational bayesian approximation of log determinants"):
  Abstract: Evaluating the log determinant of a positive definite matrix is ubiquitous in machine learning. Applications thereof range from Gaussian processes, minimum-volume ellipsoids, metric learning, kernel learning, Bayesian neural networks, Determinental Point Processes, Markov random fields to partition functions of discrete graphical models. In order to avoid the canonical, yet prohibitive, Cholesky $\mathcal{O}(n^{3})$ computational cost, we propose a novel approach, with complexity $\mathcal{O}(n^{2})$, based on a constrained variational Bayes algorithm. We compare our method to Taylor, Chebyshev and Lanczos approaches and show state of the art performance on both synthetic and real-world datasets.

Includes members:
  Member "entropic trace estimates for log determinants":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "backpropagation for implicit spectral densities")
  Member "faster greedy map inference for determinantal point processes":
    also connected through other hyperedges:
      (none)
  Member "improved stochastic trace estimation using mutually unbiased bases":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration")
  Member "fast dpp sampling for nystr om with application to kernel methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "submodular batch selection for training deep neural networks")
  Member "a randomized algorithm for approximating the log determinant of a symmetric positive definite matrix":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "schatten norms in matrix streams hello sparsity goodbye dimension")
  Member "improved bounds on sample size for implicit matrix trace estimators":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "streaming low rank matrix approximation with an application to scientific simulation")

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)