human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "component spd matrices a low dimensional discriminative data descriptor for image set classification"):
  Abstract: In pattern recognition, the task of image set classification has often been performed by representing data using symmetric positive definite (SPD) matrices, in conjunction with the metric of the resulting Riemannian manifold. In this paper, we propose a new data representation framework for image sets which we call component symmetric positive definite representation (CSPD). Firstly, we obtain sub-image sets by dividing the images in the set into square blocks of the same size, and use a traditional SPD model to describe them. Then, we use the Riemannian kernel to determine similarities of corresponding subimage sets. Finally, the CSPD matrix appears in the form of the kernel matrix for all the sub-image sets; its i, j-th entry measures the similarity between the i-th and j-th sub-image sets. The Riemannian kernel is shown to satisfy Mercer’s theorem, so the CSPD matrix is symmetric and positive definite, and also lies on a Riemannian manifold. Test on three benchmark datasets shows that CSPD is both lower-dimensional and more discriminative data descriptor than standard SPD for the task of image set classification.

Includes members:
  Member "dimensionality reduction on spd manifolds the emergence of geometry aware methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hallucinating idt descriptors and i3d optical flow features for action recognition with cnns")
  Member "riemannian dictionary learning and sparse coding for positive definite matrices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sparse coding of shape trajectories for facial expression and action recognition")
  Member "sparse coding on symmetric positive definite manifolds using bregman divergences":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalized visual information analysis via tensorial algebra")

Question: Please tell me which class the source paper 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)