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: "supervised logeuclidean metric learning for symmetric positive definite matrices"
  Abstract: Metric learning has been shown to be highly effective to improve the performance of nearest neighbor classification. In this paper, we address the problem of metric learning for Symmetric Positive Definite (SPD) matrices such as covariance matrices, which arise in many real-world applications. Naively using standard Mahalanobis metric learning methods under the Euclidean geometry for SPD matrices is not appropriate, because the difference of SPD matrices can be a non-SPD matrix and thus the obtained solution can be uninterpretable. To cope with this problem, we propose to use a properly parameterized LogEuclidean distance and optimize the metric with respect to kernel-target alignment, which is a supervised criterion for kernel learning. Then the resulting non-trivial optimization problem is solved by utilizing the Riemannian geometry. Finally, we experimentally demonstrate the usefulness of our LogEuclidean metric learning algorithm on real-world classification tasks for EEG signals and texture patches.

Connected through hyperedge HE-1 (induced by source paper "riemannian batch normalization for spd neural networks"):
  with members:
    - "a statistical recurrent model on the manifold of symmetric positive definite matrices"
    - "manifoldnet a deep network framework for manifold valued data"
    - "learning a robust representation via a deep network on symmetric positive definite manifolds"
    - "when kernel methods meet feature learning log covariance network for action recognition from skeletal data"
    - "deepkspd learning kernel matrix based spd representation for fine grained image recognition"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"
    - "building deep networks on grassmann manifolds"
    - "deep learning on lie groups for skeleton based action recognition"

Connected through hyperedge HE-2 (induced by source paper "geometry aware similarity learning on spd manifolds for visual recognition"):
  with members:
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"
    - "a riemannian network for spd matrix learning"
    - "kernel methods on the riemannian manifold of symmetric positive definite matrices"
    - "bregman divergences for infinite dimensional covariance matrices"
    - "from manifold to manifold geometry aware dimensionality reduction for spd matrices"
    - "sparse coding on symmetric positive definite manifolds using bregman divergences"
    - "sparse coding and dictionary learning for symmetric positive definite matrices a kernel approach"
    - "algorithms for learning kernels based on centered alignment"

Connected through hyperedge HE-3 (induced by source paper "dimensionality reduction on spd manifolds the emergence of geometry aware methods"):
  with members:
    - "a riemannian network for spd matrix learning"
    - "kernel methods on riemannian manifolds with gaussian rbf kernels"
    - "geodesic exponential kernels when curvature and linearity conflict"
    - "from manifold to manifold geometry aware dimensionality reduction for spd matrices"
    - "sparse coding on symmetric positive definite manifolds using bregman divergences"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "spatio temporal covariance descriptors for action and gesture recognition"

Connected through hyperedge HE-4 (induced by source paper "stochastic dykstra algorithms for metric learning on positive semi definite cone"):
  with members:
    - "riemannian metric learning for symmetric positive definite matrices"
    - "finito a faster permutable incremental gradient method for big data problems"

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)