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: "optimizing over radial kernels on compact manifolds"
  Abstract: We tackle the problem of optimizing over all possible positive definite radial kernels on Riemannian manifolds for classification. Kernel methods on Riemannian manifolds have recently become increasingly popular in computer vision. However, the number of known positive definite kernels on manifolds remain very limited. Furthermore, most kernels typically depend on at least one parameter that needs to be tuned for the problem at hand. A poor choice of kernel, or of parameter value, may yield significant performance drop-off. Here, we show that positive definite radial kernels on the unit n-sphere, the Grassmann manifold and Kendall's shape manifold can be expressed in a simple form whose parameters can be automatically optimized within a support vector machine framework. We demonstrate the benefits of our kernel learning algorithm on object, face, action and shape recognition.

Connected through hyperedge HE-1 (induced by source paper "sketch specific data augmentation for freehand sketch recognition"):
  with members:
    - "object category understanding via eye fixations on freehand sketches"
    - "sketchparse towards rich descriptions for poorly drawn sketches using multi task hierarchical deep networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "enabling my robot to play pictionary recurrent neural networks for sketch recognition"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"
    - "scribbler controlling deep image synthesis with sketch and color"
    - "sketch based 3d shape retrieval using convolutional neural networks"
    - "accurate image super resolution using very deep convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "comparative evaluation of action recognition methods via riemannian manifolds fisher vectors and gmms ideal and challenging conditions"):
  with members:
    - "joint recognition and segmentation of actions via probabilistic integration of spatio temporal fisher vectors"
    - "bags of affine subspaces for robust object tracking"
    - "kernel methods on the riemannian manifold of symmetric positive definite matrices"
    - "learning discriminative stein kernel for spd matrices and its applications"
    - "multi action recognition via stochastic modelling of optical flow and gradients"
    - "log euclidean bag of words for human action recognition"
    - "random projections on manifolds of symmetric positive definite matrices for image classification"
    - "dictionary learning and sparse coding on grassmann manifolds an extrinsic solution"

Connected through hyperedge HE-3 (induced by source paper "kernelized lrr on grassmann manifolds for subspace clustering"):
  with members:
    - "low rank representation on grassmann manifolds an extrinsic perspective"
    - "expanding the family of grassmannian kernels an embedding perspective"
    - "dictionary learning and sparse coding on grassmann manifolds an extrinsic solution"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "robust principal component analysis"
    - "a tutorial on spectral clustering"
    - "principal manifolds and nonlinear dimension reduction via local tangent space alignment"

Connected through hyperedge HE-4 (induced by source paper "kernelized low rank representation on grassmann manifolds"):
  with members:
    - "expanding the family of grassmannian kernels an embedding perspective"
    - "dictionary learning and sparse coding on grassmann manifolds an extrinsic solution"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse coding and dictionary learning for symmetric positive definite matrices a kernel approach"
    - "sparse subspace clustering algorithm theory and applications"
    - "compressive acquisition of dynamic scenes"
    - "robust principal component analysis"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-5 (induced by source paper "geodesic exponential kernels when curvature and linearity conflict"):
  with members:
    - "kernel methods on riemannian manifolds with gaussian rbf kernels"
    - "kernel methods on the riemannian manifold of symmetric positive definite matrices"
    - "the varifold representation of nonoriented shapes for diffeomorphic registration"
    - "shortest path distance in random k nearest neighbor graphs"

Connected through hyperedge HE-6 (induced by source paper "expanding the family of grassmannian kernels an embedding perspective"):
  with members:
    - "kernel methods on the riemannian manifold of symmetric positive definite matrices"
    - "dictionary learning and sparse coding on grassmann manifolds an extrinsic solution"

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)