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 "flexible modeling of diversity with strongly log concave distributions"):
  Abstract: Strongly log-concave (SLC) distributions are a rich class of discrete probability distributions over subsets of some ground set. They are strictly more general than strongly Rayleigh (SR) distributions such as the well-known determinantal point process. While SR distributions offer elegant models of diversity, they lack an easy control over how they express diversity. We propose SLC as the right extension of SR that enables easier, more intuitive control over diversity, illustrating this via examples of practical importance. We develop two fundamental tools needed to apply SLC distributions to learning and inference: sampling and mode finding. For sampling we develop an MCMC sampler and give theoretical mixing time bounds. For mode finding, we establish a weak log-submodularity property for SLC functions and derive optimization guarantees for a distorted greedy algorithm.

Includes members:
  Member "submodular maximization beyond non negativity guarantees fast algorithms and applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "regularized submodular maximization at scale")
  Member "fair and diverse dpp based data summarization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "interventions for ranking in the presence of implicit bias")
  Member "log concave polynomials ii high dimensional walks and an fpras for counting bases of a matroid":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spectral independence in high dimensional expanders and applications to the hardcore model")
  Member "gdpp learning diverse generations using determinantal point process":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "padgan a generative adversarial network for performance augmented diverse designs")
  Member "guess free maximization of submodular and linear sums":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "regularized submodular maximization at scale")
  Member "unbiased estimates for linear regression via volume sampling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "blinkml efficient maximum likelihood estimation with probabilistic guarantees")
  Member "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improved guarantees and a multiple descent curve for the column subset selection problem and the nystr om method")
  Member "kronecker determinantal point processes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning nonsymmetric determinantal point processes")

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)