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 deterministic bounds on the total variation distance between univariate mixtures"):
  Abstract: The total variation distance is a core statistical distance between probability measures that satisfies the metric axioms, with value always falling in $[0,1]$. This distance plays a fundamental role in machine learning and signal processing: It is a member of the broader class of $f$-divergences, and it is related to the probability of error in Bayesian hypothesis testing. Since the total variation distance does not admit closed-form expressions for statistical mixtures (like Gaussian mixture models), one often has to rely in practice on costly numerical integrations or on fast Monte Carlo approximations that however do not guarantee deterministic lower and upper bounds. In this work, we consider two methods for bounding the total variation of univariate mixture models: The first method is based on the information monotonicity property of the total variation to design guaranteed nested deterministic lower bounds. The second method relies on computing the geometric lower and upper envelopes of weighted mixture components to derive deterministic bounds based on density ratio. We demonstrate the tightness of our bounds in a series of experiments on Gaussian, Gamma and Rayleigh mixture models.

Includes members:
  Member "guaranteed bounds on information theoretic measures of univariate mixtures using piecewise log sum exp inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a generalization of the α divergences based on comparable and distinct weighted means")
  Member "f divergence inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on implicit regularization in beta vaes")
  Member "on reverse pinsker inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sample efficient model free reinforcement learning with off policy critics")
  Member "on the chi square and higher order chi distances for approximating f divergences":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the jensen shannon symmetrization of distances relying on abstract means")
  Member "generalized bhattacharyya and chernoff upper bounds on bayes error using quasi arithmetic means":
    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 "generalised pinsker inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on w mixtures finite convex combinations of prescribed component distributions")
  Member "statistical exponential families a digest with flash cards":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the jensen shannon symmetrization of distances relying on abstract means")

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)