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 "ts net combining modality specific and common features for multimodal patch matching"):
  Abstract: Multimodal patch matching addresses the problem of finding the correspondences between image patches from two different modalities, e.g. RGB vs sketch or RGB vs near-infrared. The comparison of patches of different modalities can be done by discovering the information common to both modalities (Siamese like approaches) or the modality-specific information (Pseudo-Siamese like approaches). We observed that none of these two scenarios is optimal. This motivates us to propose a three-stream architecture, dubbed as TS-Net, combining the benefits of the two. In addition, we show that adding extra constraints in the intermediate layers of such networks further boosts the performance. Experimentations on three multimodal datasets show significant performance gains in comparison with Siamese and Pseudo-Siamese networks.

Includes members:
  Member "pn net conjoined triple deep network for learning local image descriptors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "contextdesc local descriptor augmentation with cross modality context")
  Member "stereo matching by training a convolutional neural network to compare image patches":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mini unmanned aerial vehicle based remote sensing techniques applications and prospects")
  Member "learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "when relation networks meet gans relation gans with triplet loss")
  Member "learning to compare image patches via convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "metric and kernel learning using a linear transformation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi modal music information retrieval augmenting audio analysis with visual computing for improved music video analysis")

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)