human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "on w mixtures finite convex combinations of prescribed component distributions"
  Abstract: We consider the space of $w$-mixtures that is the set of finite statistical mixtures sharing the same prescribed component distributions. The geometry induced by the Kullback-Leibler (KL) divergence on this family of $w$-mixtures is a dually flat space in information geometry called the mixture family manifold. It follows that the KL divergence between two $w$-mixtures is equivalent to a Bregman Divergence (BD) defined for the negative Shannon entropy generator. Thus the KL divergence between two Gaussian Mixture Models (GMMs) sharing the same components is (theoretically) a Bregman divergence. This KL-BD equivalence implies that we can perform optimal KL-averaging aggregation of $w$-mixtures without information loss. More generally, we prove that the skew Jensen-Shannon divergence between $w$-mixtures is equivalent to a skew Jensen divergence on their parameters. Finally, we state several divergence identity and inequalities relating $w$-mixtures.

Connected through hyperedge HE-1 (induced by source paper "on the chain rule optimal transport distance"):
  with members:
    - "guaranteed deterministic bounds on the total variation distance between univariate mixtures"
    - "monte carlo information geometry the dually flat case"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "guaranteed bounds on information theoretic measures of univariate mixtures using piecewise log sum exp inequalities"
    - "on the chi square and higher order chi distances for approximating f divergences"
    - "generalized bhattacharyya and chernoff upper bounds on bayes error using quasi arithmetic means"
    - "a closed form expression for the sharma mittal entropy of exponential families"
    - "on renyi and tsallis entropies and divergences for exponential families"

Connected through hyperedge HE-2 (induced by source paper "monte carlo information geometry the dually flat case"):
  with members:
    - "fast exact k means k medians and bregman divergence clustering in 1d"
    - "guaranteed bounds on information theoretic measures of univariate mixtures using piecewise log sum exp inequalities"
    - "optimal interval clustering application to bregman clustering and statistical mixture learning"
    - "the burbea rao and bhattacharyya centroids"
    - "a family of statistical symmetric divergences based on jensen s inequality"
    - "statistical exponential families a digest with flash cards"

Connected through hyperedge HE-3 (induced by source paper "distance formulas capable of unifying euclidian space and probability space"):
  with members:
    - "which distribution distances are sublinearly testable"

Question: Please tell me which class the center node 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)