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 "learning a robust representation via a deep network on symmetric positive definite manifolds"):
  Abstract: Recent studies have shown that aggregating convolutional features of a pre-trained Convolutional Neural Network (CNN) can obtain impressive performance for a variety of visual tasks. The symmetric Positive Definite (SPD) matrix becomes a powerful tool due to its remarkable ability to learn an appropriate statistic representation to characterize the underlying structure of visual features. In this paper, we propose to aggregate deep convolutional features into an SPD matrix representation through the SPD generation and the SPD transformation under an end-to-end deep network. To this end, several new layers are introduced in our network, including a nonlinear kernel aggregation layer, an SPD matrix transformation layer, and a vectorization layer. The nonlinear kernel aggregation layer is employed to aggregate the convolutional features into a real SPD matrix directly. The SPD matrix transformation layer is designed to construct a more compact and discriminative SPD representation. The vectorization and normalization operations are performed in the vectorization layer for reducing the redundancy and accelerating the convergence. The SPD matrix in our network can be considered as a mid-level representation bridging convolutional features and high-level semantic features. To demonstrate the effectiveness of our method, we conduct extensive experiments on visual classification. Experiment results show that our method notably outperforms state-of-the-art methods.

Includes members:
  Member "deep manifold to manifold transforming network for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spatio temporal graph convolution for skeleton based action recognition")
  Member "deepkspd learning kernel matrix based spd representation for fine grained image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a neural network based on spd manifold learning for skeleton based hand gesture recognition")
  Member "is second order information helpful for large scale visual recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "high order paired aspp networks for semantic segmenation")
  Member "second order convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mvc net a convolutional neural network architecture for manifold valued images with applications")
  Member "dimensionality reduction on spd manifolds the emergence of geometry aware methods":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hallucinating idt descriptors and i3d optical flow features for action recognition with cnns")
  Member "exploiting structure sparsity for covariance based visual representation":
    also connected through other hyperedges:
      (none)
  Member "deep ten texture encoding network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "histogram layers for texture analysis")
  Member "learning an invariant hilbert space for domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "domain adaptation through transferring both the source knowledge and target relatedness simultaneously")

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)