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 "oriented boxes for accurate instance segmentation"):
  Abstract: State-of-the-art instance-aware semantic segmentation algorithms use axis-aligned bounding boxes as an intermediate processing step to infer the final instance mask output. This leads to coarse and inaccurate mask proposals due to the following reasons: Axis-aligned boxes have a high background to foreground pixel-ratio, there is a strong variation of mask targets with respect to the underlying box, and neighboring instances frequently reach into the axis-aligned bounding box of the instance mask of interest. #R##N#In this work, we overcome these problems and propose using oriented boxes as the basis to infer instance masks. We show that oriented instance segmentation leads to very accurate mask predictions, especially when objects are diagonally aligned, touching, or overlapping each other. We evaluate our model on the D2S and Screws datasets and show that we can significantly improve the mask accuracy by 7% and 11% mAP (14.9% and 27.5% relative improvement), respectively.

Includes members:
  Member "retinamask learning to predict masks improves state of the art single shot detection for free":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi task collaborative network for joint referring expression comprehension and segmentation")
  Member "mask scoring r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "proposal learning for semi supervised object detection")
  Member "shapemask learning to segment novel objects by refining shape priors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fouriernet compact mask representation for instance segmentation using differentiable shape decoders")
  Member "acquire augment segment enjoy weakly supervised instance segmentation of supermarket products":
    also connected through other hyperedges:
      (none)
  Member "arbitrary oriented scene text detection via rotation proposals":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "mvtec d2s densely segmented supermarket dataset":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep multicameral decoding for localizing unoccluded object instances from a single rgb image")
  Member "automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "arbitrary oriented object detection with circular smooth label")
  Member "the open images dataset v4 unified image classification object detection and visual relationship detection at scale":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "revisiting the sibling head in object detector")

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)