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 "riemannian dictionary learning and sparse coding for positive definite matrices"):
  Abstract: Data encoded as symmetric positive definite (SPD) matrices frequently arise in many areas of computer vision and machine learning. While these matrices form an open subset of the Euclidean space of symmetric matrices, viewing them through the lens of non-Euclidean Riemannian geometry often turns out to be better suited in capturing several desirable data properties. However, formulating classical machine learning algorithms within such a geometry is often non-trivial and computationally expensive. Inspired by the great success of dictionary learning and sparse coding for vector-valued data, our goal in this paper is to represent data in the form of SPD matrices as sparse conic combinations of SPD atoms from a learned dictionary via a Riemannian geometric approach. To that end, we formulate a novel Riemannian optimization objective for dictionary learning and sparse coding in which the representation loss is characterized via the affine invariant Riemannian metric. We also present a computationally simple algorithm for optimizing our model. Experiments on several computer vision datasets demonstrate superior classification and retrieval performance using our approach when compared to sparse coding via alternative non-Riemannian formulations.

Includes members:
  Member "kernel methods on riemannian manifolds with gaussian rbf kernels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (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-3 (induced by source paper "deep heterogeneous hashing for face video retrieval")
  Member "sparse modeling for image and vision processing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "sparse coding on symmetric positive definite manifolds using bregman divergences":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generalized visual information analysis via tensorial algebra")
  Member "dictionary learning and sparse coding on grassmann manifolds an extrinsic solution":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online structured sparsity based moving object detection from satellite videos")
  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")

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)