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 "annotating object instances with a polygon rnn"):
  Abstract: We propose an approach for semi-automatic annotation of object instances. While most current methods treat object segmentation as a pixel-labeling problem, we here cast it as a polygon prediction task, mimicking how most current datasets have been annotated. In particular, our approach takes as input an image crop and sequentially produces vertices of the polygon outlining the object. This allows a human annotator to interfere at any time and correct a vertex if needed, producing as accurate segmentation as desired by the annotator. We show that our approach speeds up the annotation process by a factor of 4.7 across all classes in Cityscapes, while achieving 78.4% agreement in IoU with original ground-truth, matching the typical agreement between human annotators. For cars, our speed-up factor is 7.3 for an agreement of 82.2%. We further show generalization capabilities of our approach to unseen datasets.

Includes members:
  Member "the cityscapes dataset for semantic urban scene understanding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "semantic understanding of scenes through the ade20k dataset":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ego topo environment affordances from egocentric video")
  Member "pixel level encoding and depth layering for instance level semantic labeling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards bounding box free panoptic segmentation")
  Member "deepcut object segmentation from bounding box annotations using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "model based and data driven strategies in medical image computing")
  Member "learning to refine object segments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "scribblesup scribble supervised convolutional networks for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to adapt class specific features across domains for semantic segmentation")
  Member "learning to segment object candidates":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "iterative instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "object contextual representations for semantic segmentation")

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)