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: "sparse coding and dictionary learning for symmetric positive definite matrices a kernel approach"
  Abstract: Recent advances suggest that a wide range of computer vision problems can be addressed more appropriately by considering non-Euclidean geometry. This paper tackles the problem of sparse coding and dictionary learning in the space of symmetric positive definite matrices, which form a Riemannian manifold. With the aid of the recently introduced Stein kernel (related to a symmetric version of Bregman matrix divergence), we propose to perform sparse coding by embedding Riemannian manifolds into reproducing kernel Hilbert spaces. This leads to a convex and kernel version of the Lasso problem, which can be solved efficiently. We furthermore propose an algorithm for learning a Riemannian dictionary (used for sparse coding), closely tied to the Stein kernel. Experiments on several classification tasks (face recognition, texture classification, person re-identification) show that the proposed sparse coding approach achieves notable improvements in discrimination accuracy, in comparison to state-of-the-art methods such as tensor sparse coding, Riemannian locality preserving projection, and symmetry-driven accumulation of local features.

Connected through hyperedge HE-1 (induced by source paper "sparse coding of shape trajectories for facial expression and action recognition"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "a survey of automatic facial micro expression analysis databases methods and challenges"
    - "a novel geometric framework on gram matrix trajectories for human behavior understanding"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "a deep learning perspective on the origin of facial expressions"
    - "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"
    - "spontaneous subtle expression detection and recognition based on facial strain"

Connected through hyperedge HE-2 (induced by source paper "statistically motivated second order pooling"):
  with members:
    - "a deeper look at power normalizations"
    - "second order democratic aggregation"
    - "is second order information helpful for large scale visual recognition"
    - "improved bilinear pooling with cnns"
    - "low rank bilinear pooling for fine grained classification"
    - "domain adaptation by mixture of alignments of second or higher order scatter tensors"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "densely connected convolutional networks"

Connected through hyperedge HE-3 (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-4 (induced by source paper "a simple riemannian manifold network for image set classification"):
  with members:
    - "building deep networks on grassmann manifolds"
    - "wide residual networks"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "deep residual learning for image recognition"
    - "training deep networks with structured layers by matrix backpropagation"
    - "beyond gauss image set matching on the riemannian manifold of pdfs"
    - "pcanet a simple deep learning baseline for image classification"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "convex class model on symmetric positive definite manifolds"):
  with members:
    - "kernel sparse subspace clustering on symmetric positive definite manifolds"
    - "geometry aware similarity learning on spd manifolds for visual recognition"
    - "matching image sets via adaptive multi convex hull"
    - "kernel methods on the riemannian manifold of symmetric positive definite matrices"
    - "geodesic exponential kernels when curvature and linearity conflict"
    - "extrinsic methods for coding and dictionary learning on grassmann manifolds"
    - "compressive acquisition of dynamic scenes"

Connected through hyperedge HE-6 (induced by source paper "when kernel methods meet feature learning log covariance network for action recognition from skeletal data"):
  with members:
    - "joint distance maps based action recognition with convolutional neural networks"
    - "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"
    - "action recognition based on joint trajectory maps using convolutional neural networks"
    - "deep learning on lie groups for skeleton based action recognition"
    - "tensor representations via kernel linearization for action recognition from 3d skeletons"
    - "a riemannian network for spd matrix learning"

Connected through hyperedge HE-7 (induced by source paper "kernel cross view collaborative representation based classification for person re identification"):
  with members:
    - "re ranking person re identification with k reciprocal encoding"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "person re identification for real world surveillance systems"
    - "learning a discriminative null space for person re identification"
    - "deep ranking for person re identification via joint representation learning"
    - "scalable metric learning via weighted approximate rank component analysis"
    - "a siamese long short term memory architecture for human re identification"
    - "an enhanced deep feature representation for person re identification"

Connected through hyperedge HE-8 (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"
    - "optimizing over radial kernels on compact manifolds"

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)