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 "a distraction score for watermarks"):
  Abstract: In this work we propose a novel technique to quantify how distracting watermarks are on an image. We begin with watermark detection using a two-tower CNN model composed of a binary classification task and a semantic segmentation prediction. With this model, we demonstrate significant improvement in image precision while maintaining per-pixel accuracy, especially for our real-world dataset with sparse positive examples. We fit a nonlinear function to represent detected watermarks by a single score correlated with human perception based on their size, location, and visual obstructiveness. Finally, we validate our method in an image ranking setup, which is the main application of our watermark scoring algorithm.

Includes members:
  Member "encoder decoder with atrous separable convolution for semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "rethinking atrous convolution for semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "mask r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  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 "learning to segment object candidates":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "instance aware semantic segmentation via multi task network cascades":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "semantic image segmentation with deep convolutional nets and fully connected crfs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "attention guided chained context aggregation 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)