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 "guaranteed bounds on information theoretic measures of univariate mixtures using piecewise log sum exp inequalities"):
  Abstract: Information-theoretic measures, such as the entropy, the cross-entropy and the Kullback–Leibler divergence between two mixture models, are core primitives in many signal processing tasks. Since the Kullback–Leibler divergence of mixtures provably does not admit a closed-form formula, it is in practice either estimated using costly Monte Carlo stochastic integration, approximated or bounded using various techniques. We present a fast and generic method that builds algorithmically closed-form lower and upper bounds on the entropy, the cross-entropy, the Kullback–Leibler and the α-divergences of mixtures. We illustrate the versatile method by reporting our experiments for approximating the Kullback–Leibler and the α-divergences between univariate exponential mixtures, Gaussian mixtures, Rayleigh mixtures and Gamma mixtures.

Includes members:
  Member "a tight convex upper bound on the likelihood of a finite mixture":
    also connected through other hyperedges:
      (none)
  Member "on the chi square and higher order chi distances for approximating f divergences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the jensen shannon symmetrization of distances relying on abstract means")
  Member "renyi divergence and kullback leibler divergence":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "a closed form expression for the sharma mittal entropy of exponential families":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "q vae for disentangled representation learning and latent dynamical systems")
  Member "on renyi and tsallis entropies and divergences for exponential families":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the chain rule optimal transport distance")
  Member "the burbea rao and bhattacharyya centroids":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the alpha divergences associated with a pair of strictly comparable quasi arithmetic means")
  Member "on the dual formulation of boosting algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "margin maximization as lossless maximal compression")
  Member "constructing two dimensional voronoi diagrams via divide and conquer of envelopes in space":
    also connected through other hyperedges:
      (none)

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)