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 "multi scale cell instance segmentation with keypoint graph based bounding boxes"):
  Abstract: Most existing methods handle cell instance segmentation problems directly without relying on additional detection boxes. These methods generally fails to separate touching cells due to the lack of global understanding of the objects. In contrast, box-based instance segmentation solves this problem by combining object detection with segmentation. However, existing methods typically utilize anchor box-based detectors, which would lead to inferior instance segmentation performance due to the class imbalance issue. In this paper, we propose a new box-based cell instance segmentation method. In particular, we first detect the five pre-defined points of a cell via keypoints detection. Then we group these points according to a keypoint graph and subsequently extract the bounding box for each cell. Finally, cell segmentation is performed on feature maps within the bounding boxes. We validate our method on two cell datasets with distinct object shapes, and empirically demonstrate the superiority of our method compared to other instance segmentation techniques. Code is available at: https://github.com/yijingru/KG_Instance_Segmentation.

Includes members:
  Member "cornernet detecting objects as paired keypoints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "higherhrnet scale aware representation learning for bottom up human pose estimation")
  Member "instance segmentation and tracking with cosine embeddings and recurrent hourglass networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cell r cnn v3 a novel panoptic paradigm for instance segmentation in biomedical images")
  Member "cell detection with star convex polygons":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "patchperpix for instance segmentation")
  Member "mask r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "stacked hourglass networks for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "dcan deep contour aware networks for accurate gland segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bi directional dermoscopic feature learning and multi scale consistent decision fusion for skin lesion segmentation")
  Member "fully convolutional instance aware semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep affinity net instance segmentation via affinity")

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)