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 on symmetric positive definite manifolds using bregman divergences"
  Abstract: This paper introduces sparse coding and dictionary learning for Symmetric Positive Definite (SPD) matrices, which are often used in machine learning, computer vision and related areas. Unlike traditional sparse coding schemes that work in vector spaces, in this paper we discuss how SPD matrices can be described by sparse combination of dictionary atoms, where the atoms are also SPD matrices. We propose to seek sparse coding by embedding the space of SPD matrices into Hilbert spaces through two types of Bregman matrix divergences. This not only leads to an efficient way of performing sparse coding, but also an online and iterative scheme for dictionary learning. We apply the proposed methods to several computer vision tasks where images are represented by region covariance matrices. Our proposed algorithms outperform state-of-the-art methods on a wide range of classification tasks, including face recognition, action recognition, material classification and texture categorization.

Connected through hyperedge HE-1 (induced by source paper "generalized visual information analysis via tensorial algebra"):
  with members:
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "extrinsic methods for coding and dictionary learning on grassmann manifolds"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "the power and arnoldi methods in an algebra of circulants"

Connected through hyperedge HE-2 (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-3 (induced by source paper "unsupervised assignment flow label learning on feature manifolds by spatially regularized geometric assignment"):
  with members:
    - "image labeling by assignment"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"

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 "geomstats a python package for riemannian geometry in machine learning"):
  with members:
    - "orthogonal weight normalization solution to optimization over multiple dependent stiefel manifolds in deep neural networks"
    - "geometric deep learning going beyond euclidean data"
    - "poincar e embeddings for learning hierarchical representations"
    - "computational anatomy in theano"
    - "pymanopt a python toolbox for optimization on manifolds using automatic differentiation"
    - "manopt a matlab toolbox for optimization on manifolds"

Connected through hyperedge HE-6 (induced by source paper "riemannian kernel based nystrom method for approximate infinite dimensional covariance descriptors with application to image set classification"):
  with members:
    - "multi focus image fusion using dictionary learning and low rank representation"
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"
    - "riemannian dictionary learning and sparse coding for positive definite matrices"

Connected through hyperedge HE-7 (induced by source paper "component spd matrices a low dimensional discriminative data descriptor for image set classification"):
  with members:
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"
    - "riemannian dictionary learning and sparse coding for positive definite matrices"

Connected through hyperedge HE-8 (induced by source paper "locality preserving projections for grassmann manifold"):
  with members:
    - "kernel methods on riemannian manifolds with gaussian rbf kernels"
    - "from manifold to manifold geometry aware dimensionality reduction for spd 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)