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 "sketchdesc learning local sketch descriptors for multi view correspondence"):
  Abstract: In this paper, we study the problem of multi-view sketch correspondence, where we take as input multiple freehand sketches with different views of the same object and predict semantic correspondence among the sketches. This problem is challenging, since visual features of corresponding points at different views can be very different. To this end, we take a deep learning approach and learn a novel local sketch descriptor from data. We contribute a training dataset by generating the pixel-level correspondence for the multi-view line drawings synthesized from 3D shapes. To handle the sparsity and ambiguity of sketches, we design a novel multi-branch neural network that integrates a patch-based representation and a multi-scale strategy to learn the \pixelLevel correspondence among multi-view sketches. We demonstrate the effectiveness of our proposed approach with extensive experiments on hand-drawn sketches, and multi-view line drawings rendered from multiple 3D shape datasets.

Includes members:
  Member "sketchzooms deep multi view descriptors for matching line drawings":
    also connected through other hyperedges:
      (none)
  Member "hierarchical deep stereo matching on high resolution images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "sosnet second order similarity regularization for local descriptor learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "from handcrafted to deep local invariant features":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "r2d2 repeatable and reliable detector and descriptor")
  Member "sketchmate deep hashing for million scale human sketch retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep self supervised representation learning for free hand sketch")
  Member "sketch r2cnn an attentive network for vector sketch recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep self supervised representation learning for free hand sketch")
  Member "geodesc learning local descriptors by integrating geometry constraints":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "fast sketch segmentation and labeling with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sketchgcn semantic sketch segmentation with graph convolutional networks")

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)