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 "sketchzooms deep multi view descriptors for matching line drawings"):
  Abstract: Finding point-wise correspondences between images is a long-standing problem in computer vision. Corresponding sketch images is particularly challenging due to the varying nature of human style, projection distortions and viewport changes. In this paper we present a feature descriptor targeting line drawings learned from a 3D shape data set. Our descriptors are designed to locally match image pairs where the object of interest belongs to the same semantic category, yet still differ drastically in shape and projection angle. We build our descriptors by means of a Convolutional Neural Network (CNN) trained in a triplet fashion. The goal is to embed semantically similar anchor points close to one another, and to pull the embeddings of different points far apart. To learn the descriptors space, the network is fed with a succession of zoomed views from the input sketches. We have specifically crafted a data set of synthetic sketches using a non-photorealistic rendering algorithm over a large collection of part-based registered 3D models. Once trained, our network can generate descriptors for every pixel in an input image. Furthermore, our network is able to generalize well to unseen sketches hand-drawn by humans, outperforming state-of-the-art descriptors on the evaluated matching tasks. Our descriptors can be used to obtain sparse and dense correspondences between image pairs. We evaluate our method against a baseline of correspondences data collected from expert designers, in addition to comparisons with descriptors that have been proven effective in sketches. Finally, we demonstrate applications showing the usefulness of our multi-view descriptors.

Includes members:
  Member "neural best buddies sparse cross domain correspondence":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cross domain cascaded deep feature translation")
  Member "triplet center loss for multi view 3d object retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pf net point fractal network for 3d point cloud completion")
  Member "sampling matters in deep embedding learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning")
  Member "3d shape reconstruction from sketches via multi view convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sketchdesc learning local sketch descriptors for multi view correspondence")
  Member "mastering sketching adversarial augmentation for structured prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to shade hand drawn sketches")
  Member "a neural representation of sketch drawings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep self supervised representation learning for free hand sketch")
  Member "3d sketching using multi view deep volumetric prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep sketch based modeling of man made shapes")
  Member "learning aligned cross modal representations from weakly aligned data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "incorporating visual semantics into sentence representations within a grounded space")

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)