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: "geometric estimation of multivariate dependency"
  Abstract: This paper proposes a geometric estimator of dependency between a pair of multivariate random variables. The proposed estimator of dependency is based on a randomly permuted geometric graph (the minimal spanning tree) over the two multivariate samples. This estimator converges to a quantity that we call the geometric mutual information (GMI), which is equivalent to the Henze–Penrose divergence. between the joint distribution of the multivariate samples and the product of the marginals. The GMI has many of the same properties as standard MI but can be estimated from empirical data without density estimation; making it scalable to large datasets. The proposed empirical estimator of GMI is simple to implement, involving the construction of an minimal spanning tree (MST) spanning over both the original data and a randomly permuted version of this data. We establish asymptotic convergence of the estimator and convergence rates of the bias and variance for smooth multivariate density functions belonging to a Holder class. We demonstrate the advantages of our proposed geometric dependency estimator in a series of experiments.

Connected through hyperedge HE-1 (induced by source paper "mint deep network compression via mutual information based neuron trimming"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning to see the wood for the trees deep laser localization in urban and natural environments on a cpu"
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "deep grid net dgn a deep learning system for real time driving context understanding"
    - "scipy 1 0 fundamental algorithms for scientific computing in python"
    - "neurotrajectory a neuroevolutionary approach to local state trajectory learning for autonomous vehicles"
    - "regularized evolution for image classifier architecture search"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"

Connected through hyperedge HE-2 (induced by source paper "a geometric approach to online streaming feature selection"):
  with members:
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "online unsupervised multi view feature selection"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "geometric online adaptation graph based osfs for streaming samples"):
  with members:
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "online unsupervised multi view feature selection"
    - "deep residual learning for image recognition"

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)