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: "kernel methods on riemannian manifolds with gaussian rbf kernels"
  Abstract: In this paper, we develop an approach to exploiting kernel methods with manifold-valued data. In many computer vision problems, the data can be naturally represented as points on a Riemannian manifold. Due to the non-Euclidean geometry of Riemannian manifolds, usual Euclidean computer vision and machine learning algorithms yield inferior results on such data. In this paper, we define Gaussian radial basis function (RBF)-based positive definite kernels on manifolds that permit us to embed a given manifold with a corresponding metric in a high dimensional reproducing kernel Hilbert space. These kernels make it possible to utilize algorithms developed for linear spaces on nonlinear manifold-valued data. Since the Gaussian RBF defined with any given metric is not always positive definite, we present a unified framework for analyzing the positive definiteness of the Gaussian RBF on a generic metric space. We then use the proposed framework to identify positive definite kernels on two specific manifolds commonly encountered in computer vision: the Riemannian manifold of symmetric positive definite matrices and the Grassmann manifold, i.e., the Riemannian manifold of linear subspaces of a Euclidean space. We show that many popular algorithms designed for Euclidean spaces, such as support vector machines, discriminant analysis and principal component analysis can be generalized to Riemannian manifolds with the help of such positive definite Gaussian kernels.

Connected through hyperedge HE-1 (induced by source paper "person re identification in aerial imagery"):
  with members:
    - "omni scale feature learning for person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "learning rich features for image manipulation detection"
    - "vision meets drones a challenge"
    - "learning discriminative features with multiple granularities for person re identification"
    - "dissecting person re identification from the viewpoint of viewpoint"
    - "harmonious attention network for person re identification"
    - "person re identification with deep similarity guided graph neural network"

Connected through hyperedge HE-2 (induced by source paper "bayesian optimization meets riemannian manifolds in robot learning"):
  with members:
    - "riemannian motion policies"
    - "bayesian optimization with automatic prior selection for data efficient direct policy search"
    - "deep kernels for optimizing locomotion controllers"
    - "deep reinforcement learning a brief survey"
    - "bayesian optimization using domain knowledge on the atrias biped"
    - "on the design of lqr kernels for efficient controller learning"
    - "pymanopt a python toolbox for optimization on manifolds using automatic differentiation"
    - "learning dynamic robot to human object handover from human feedback"

Connected through hyperedge HE-3 (induced by source paper "riemannian stochastic variance reduced gradient algorithm with retraction and vector transport"):
  with members:
    - "riemannian stochastic quasi newton algorithm with variance reduction and its convergence analysis"
    - "riemannian stochastic variance reduced gradient on grassmann manifold"
    - "fast stochastic algorithms for svd and pca convergence properties and convexity"
    - "sdca without duality"
    - "improved svrg for non strongly convex or sum of non convex objectives"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "online identification and tracking of subspaces from highly incomplete information"

Connected through hyperedge HE-4 (induced by source paper "automatic analysis of facial expressions based on deep covariance trajectories"):
  with members:
    - "deep covariance descriptors for facial expression recognition"
    - "covariance pooling for facial expression recognition"
    - "a novel geometric framework on gram matrix trajectories for human behavior understanding"
    - "island loss for learning discriminative features in facial expression recognition"
    - "automatic recognition of facial displays of unfelt emotions"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "going deeper in facial expression recognition using deep neural networks"
    - "peak piloted deep network for facial expression recognition"

Connected through hyperedge HE-5 (induced by source paper "a novel geometric framework on gram matrix trajectories for human behavior understanding"):
  with members:
    - "emotion recognition by body movement representation on the manifold of symmetric positive definite matrices"
    - "elastic functional coding of riemannian trajectories"
    - "kernelized covariance for action recognition"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"
    - "shape distributions of nonlinear dynamical systems for video based inference"

Connected through hyperedge HE-6 (induced by source paper "a novel space time representation on the positive semidefinite con for facial expression recognition"):
  with members:
    - "elastic functional coding of riemannian trajectories"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "survey on rgb 3d thermal and multimodal approaches for facial expression recognition history trends and affect related applications"
    - "spatio temporal covariance descriptors for action and gesture recognition"

Connected through hyperedge HE-7 (induced by source paper "riemannian stochastic quasi newton algorithm with variance reduction and its convergence analysis"):
  with members:
    - "riemannian stochastic variance reduced gradient algorithm with retraction and vector transport"
    - "low rank tensor completion a riemannian manifold preconditioning approach"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "manopt a matlab toolbox for optimization on manifolds"

Connected through hyperedge HE-8 (induced by source paper "evolutionary image composition using feature covariance matrices"):
  with members:
    - "the evolutionary process of image transition in conjunction with box and strip mutation"
    - "optimal parameter choices through self adjustment applying the 1 5 th rule in discrete settings"
    - "kernel methods on the riemannian manifold of symmetric positive definite matrices"

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.CV(Computer Vision and Pattern Recognition)