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 "semantic edge detection with diverse deep supervision"):
  Abstract: Semantic edge detection (SED), which aims at jointly extracting edges as well as their category information, has far-reaching applications in domains such as semantic segmentation, object proposal generation, and object recognition. SED naturally requires achieving two distinct supervision targets: locating fine detailed edges and identifying high-level semantics. We shed light on how such distracted supervision targets prevent state-of-the-art SED methods from effectively using deep supervision to improve results. In this paper, we propose a novel fully convolutional neural network architecture using diverse deep supervision (DDS) within a multi-task framework where lower layers aim at generating category-agnostic edges, while higher layers are responsible for the detection of category-aware semantic edges. To overcome the distracted supervision challenge, a novel information converter unit is introduced, whose effectiveness has been extensively evaluated in several popular benchmark datasets, including SBD, Cityscapes, and PASCAL VOC2012. Source code will be released upon paper acceptance.

Includes members:
  Member "devil is in the edges learning semantic boundaries from noisy annotations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cafenet class agnostic few shot edge detection network")
  Member "deep crisp boundaries from boundaries to higher level tasks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "object contour and edge detection with refinecontournet")
  Member "dynamic feature fusion for semantic edge detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cafenet class agnostic few shot edge detection network")
  Member "deeply supervised salient object detection with short connections":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "three birds one stone a unified framework for salient object segmentation edge detection and skeleton extraction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "taskonomy disentangling task transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mti net multi scale task interaction networks for multi task learning")
  Member "encoder decoder with atrous separable convolution for semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "simultaneous edge alignment and learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep multicameral decoding for localizing unoccluded object instances from a single rgb image")

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)