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 to approximate directional fields defined over 2d planes"):
  Abstract: Reconstruction of directional fields is a need in many geometry processing tasks, such as image tracing, extraction of 3D geometric features, and finding principal surface directions. A common approach to the construction of directional fields from data relies on complex optimization procedures, which are usually poorly formalizable, require a considerable computational effort, and do not transfer across applications. In this work, we propose a deep learning-based approach and study the expressive power and generalization ability.

Includes members:
  Member "procedural synthesis of remote sensing images for robust change detection with neural networks":
    also connected through other hyperedges:
      (none)
  Member "monocular 3d object detection via geometric reasoning on keypoints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "monopair monocular 3d object detection using pairwise spatial relationships")
  Member "boundary loss for remote sensing imagery semantic segmentation":
    also connected through other hyperedges:
      (none)
  Member "vectorization of line drawings via polyvector fields":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep vectorization of technical drawings")
  Member "targeted change detection in remote sensing images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "procedural synthesis of remote sensing images for robust change detection with neural networks")
  Member "perceptual deep depth super resolution":
    also connected through other hyperedges:
      (none)
  Member "abc a big cad model dataset for geometric deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep vectorization of technical drawings")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secure and robust machine learning for healthcare a survey")

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)