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 "spatialflow bridging all tasks for panoptic segmentation"):
  Abstract: Object location is fundamental to panoptic segmentation as it is related to all things and stuff. How to integrate object location in both thing and stuff segmentation is a crucial problem. In this paper, we propose object spatial information flows to achieve this objective. More importantly, we design four parallel sub-networks for sub-tasks in panoptic segmentation, which leads to the preferable adaptation of object spatial information. With sub-networks, the flows can bridge all tasks together by delivering the object's spatial context from the box regression task to others. They can also provide clues for segmenting both things and stuff, which helps the network better understand the whole image. Upon the sub-networks and the flows, we present a location-aware and unified framework for panoptic segmentation, denoted as SpatialFlow. We perform a detailed ablation study on each component and conduct extensive experiments to prove the effectiveness of Our SpatialFlow. Furthermore, we achieve state-of-the-art results, which are $47.3$ PQ and $62.5$ PQ respectively on MS-COCO and Cityscapes panoptic benchmarks.

Includes members:
  Member "panoptic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "retinamask learning to predict masks improves state of the art single shot detection for free":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi task collaborative network for joint referring expression comprehension and segmentation")
  Member "an end to end network for panoptic segmentation":
    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 "region proposal by guided anchoring":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "udd an underwater open sea farm object detection dataset for underwater robot picking")
  Member "upsnet a unified panoptic segmentation network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep affinity net instance segmentation via affinity")
  Member "box driven class wise region masking and filling rate guided loss for weakly supervised semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "realizing pixel level semantic learning in complex driving scenes based on only one annotated pixel per class")
  Member "deeperlab single shot image parser":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "higherhrnet scale aware representation learning for bottom up human pose estimation")
  Member "mmdetection open mmlab detection toolbox and benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mixpath a unified approach for one shot neural architecture search")

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)