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 "dimensionality reduction on spd manifolds the emergence of geometry aware methods"):
  Abstract: Representing images and videos with Symmetric Positive Definite (SPD) matrices, and considering the Riemannian geometry of the resulting space, has been shown to yield high discriminative power in many visual recognition tasks. Unfortunately, computation on the Riemannian manifold of SPD matrices -especially of high-dimensional ones- comes at a high cost that limits the applicability of existing techniques. In this paper, we introduce algorithms able to handle high-dimensional SPD matrices by constructing a lower-dimensional SPD manifold. To this end, we propose to model the mapping from the high-dimensional SPD manifold to the low-dimensional one with an orthonormal projection. This lets us formulate dimensionality reduction as the problem of finding a projection that yields a low-dimensional manifold either with maximum discriminative power in the supervised scenario, or with maximum variance of the data in the unsupervised one. We show that learning can be expressed as an optimization problem on a Grassmann manifold and discuss fast solutions for special cases. Our evaluation on several classification tasks evidences that our approach leads to a significant accuracy gain over state-of-the-art methods.

Includes members:
  Member "a riemannian network for spd matrix learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "riemannian batch normalization for spd neural networks")
  Member "kernel methods on riemannian manifolds with gaussian rbf kernels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "person re identification in aerial imagery")
  Member "geodesic exponential kernels when curvature and linearity conflict":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bayesian optimization meets riemannian manifolds in robot learning")
  Member "from manifold to manifold geometry aware dimensionality reduction for spd matrices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "generalized visual information analysis via tensorial algebra")
  Member "manopt a matlab toolbox for optimization on manifolds":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "joint constellation design for the two user non coherent multiple access channel")
  Member "spatio temporal covariance descriptors for action and gesture recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a neural network based on spd manifold learning for skeleton based hand gesture 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)