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: "statistical exponential families a digest with flash cards"
  Abstract: This document describes concisely the ubiquitous class of exponential family distributions met in statistics. The first part recalls definitions and summarizes main properties and duality with Bregman divergences (all proofs are skipped). The second part lists decompositions and related formula of common exponential family distributions. We recall the Fisher-Rao-Riemannian geometries and the dual affine connection information geometries of statistical manifolds. It is intended to maintain and update this document and catalog by adding new distribution items.

Connected through hyperedge HE-1 (induced by source paper "on the jensen shannon symmetrization of distances relying on abstract means"):
  with members:
    - "on power chi expansions of f divergences"
    - "an elementary introduction to information geometry"
    - "monte carlo information geometry the dually flat case"
    - "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"
    - "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"
    - "bootstrap methods for the empirical study of decision making and information flows in social systems"

Connected through hyperedge HE-2 (induced by source paper "a probabilistic representation of deep learning"):
  with members:
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "exploring generalization in deep learning"
    - "emergence of invariance and disentanglement in deep representations"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "opening the black box of deep neural networks via information"
    - "nearly tight vc dimension and pseudodimension bounds for piecewise linear neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "conditional random fields as recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "explicitly bayesian regularizations in deep learning"):
  with members:
    - "implicit regularization in deep matrix factorization"
    - "a synthetic dataset for deep learning"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "exploring generalization in deep learning"
    - "sharp minima can generalize for deep nets"
    - "understanding deep learning requires rethinking generalization"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "an online learning approach to model predictive control"):
  with members:
    - "introduction to online convex optimization"
    - "mirror descent search and its acceleration"
    - "autorally an open platform for aggressive autonomous driving"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "vision based high speed driving with a deep dynamic observer"
    - "acceleration of gradient based path integral method for efficient optimal and inverse optimal control"
    - "adaptive online learning in dynamic environments"
    - "risk sensitive path integral control"

Connected through hyperedge HE-5 (induced by source paper "stochastic implicit natural gradient for black box optimization"):
  with members:
    - "diagonal acceleration for covariance matrix adaptation evolution strategies"
    - "provable smoothness guarantees for black box variational inference"
    - "efficient batch black box optimization with deterministic regret bounds"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "towards autonomous reinforcement learning automatic setting of hyper parameters using bayesian optimization"
    - "conjugate computation variational inference converting variational inference in non conjugate models to inferences in conjugate models"
    - "relative loss bounds for on line density estimation with the exponential family of distributions"

Connected through hyperedge HE-6 (induced by source paper "on geodesic triangles with right angles in a dually flat space"):
  with members:
    - "on a convergence property of a geometrical algorithm for statistical manifolds"
    - "an elementary introduction to information geometry"
    - "monte carlo information geometry the dually flat case"
    - "topological data analysis with bregman divergences"
    - "cramer rao lower bound and information geometry"
    - "the burbea rao and bhattacharyya centroids"

Connected through hyperedge HE-7 (induced by source paper "optimal learning of mallows block model"):
  with members:
    - "efficiently learning mixtures of mallows models"
    - "learning mixtures of ranking models"
    - "consensus ranking under the exponential model"
    - "content modeling using latent permutations"
    - "sorting from noisy information"

Connected through hyperedge HE-8 (induced by source paper "on power chi expansions of f divergences"):
  with members:
    - "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"

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)