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 "kronecker determinantal point processes"):
  Abstract: Determinantal Point Processes (DPPs) are probabilistic models over all subsets a ground set of $N$ items. They have recently gained prominence in several applications that rely on "diverse" subsets. However, their applicability to large problems is still limited due to the $\mathcal O(N^3)$ complexity of core tasks such as sampling and learning. We enable efficient sampling and learning for DPPs by introducing KronDPP, a DPP model whose kernel matrix decomposes as a tensor product of multiple smaller kernel matrices. This decomposition immediately enables fast exact sampling. But contrary to what one may expect, leveraging the Kronecker product structure for speeding up DPP learning turns out to be more difficult. We overcome this challenge, and derive batch and stochastic optimization algorithms for efficiently learning the parameters of a KronDPP.

Includes members:
  Member "fast dpp sampling for nystr om with application to kernel methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "submodular batch selection for training deep neural networks")
  Member "fixed point algorithms for learning determinantal point processes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "diversity in machine learning")
  Member "optimizing neural networks with kronecker factored approximate curvature":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "efficient sampling for k determinantal point processes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "blinkml efficient maximum likelihood estimation with probabilistic guarantees")
  Member "diversifying sparsity using variational determinantal point processes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "composable core sets for determinant maximization a simple near optimal algorithm")
  Member "learning mixtures of submodular shells with application to document summarization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust submodular minimization with applications to cooperative modeling")
  Member "solving the apparent diversity accuracy dilemma of recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

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)