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 "geometry aware similarity learning on spd manifolds for visual recognition"):
  Abstract: Symmetric Positive Definite (SPD) matrices have been widely used for data representation in many visual recognition tasks. The success mainly attributes to learning discriminative SPD matrices with encoding the Riemannian geometry of the underlying SPD manifold. In this paper, we propose a geometry-aware SPD similarity learning (SPDSL) framework to learn discriminative SPD features by directly pursuing manifold-manifold transformation matrix of column full-rank. Specifically, by exploiting the Riemannian geometry of the manifold of fixed-rank Positive Semidefinite (PSD) matrices, we present a new solution to reduce optimizing over the space of column full-rank transformation matrices to optimizing on the PSD manifold which has a well-established Riemannian structure. Under this solution, we exploit a new supervised SPD similarity learning technique to learn the transformation by regressing the similarities of selected SPD data pairs to their ground-truth similarities on the target SPD manifold. To optimize the proposed objective function, we further derive an algorithm on the PSD manifold. Evaluations on three visual classification tasks show the advantages of the proposed approach over the existing SPD-based discriminant learning methods.

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 "a riemannian network for spd matrix learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep heterogeneous hashing for face video retrieval")
  Member "supervised logeuclidean metric learning for symmetric positive definite matrices":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "riemannian batch normalization for spd neural networks")
  Member "kernel methods on the riemannian manifold of symmetric positive definite matrices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "identity preserving face recovery from stylized portraits")
  Member "bregman divergences for infinite dimensional covariance matrices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "more about covariance descriptors for image set coding log euclidean framework based kernel matrix representation")
  Member "from manifold to manifold geometry aware dimensionality reduction for spd matrices":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep heterogeneous hashing for face video retrieval")
  Member "sparse coding on symmetric positive definite manifolds using bregman divergences":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generalized visual information analysis via tensorial algebra")
  Member "sparse coding and dictionary learning for symmetric positive definite matrices a kernel approach":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sparse coding of shape trajectories for facial expression and action recognition")

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)