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 "learning instance occlusion for panoptic segmentation"):
  Abstract: The recently proposed panoptic segmentation task segments both "things" (countable object instances) and "stuff" (amorphous regions) within a single output. A common approach involves the fusion of instance segmentation (for "things") and semantic segmentation (for "stuff") proposals into a non-overlapping placement of segments, and resolves occlusions (or overlaps) between segments based on confidence scores. However, we observe that instance ordering with detection confidence does not correlate well with natural occlusion relationship, e.g., "a bowl sits on top of a dining table." To resolve this issue, we propose to add a branch that is tasked with modeling how two instance masks should overlap one another as a binary relation. Our method, named OCFusion, is lightweight but particularly effective on the "things" portion of the standard panoptic segmentation datasets, bringing significant gains (up to +3.2 PQ^Th and +2.0 overall PQ) on COCO panoptic segmentation benchmark, even when minimally fine-tuned (for few thousand iterations) on top of pretrained Panoptic FPN baseline. We show that OCFusion generalizes across different architectures and datasets.

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 "an end to end network for panoptic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cell r cnn v3 a novel panoptic paradigm for instance segmentation in biomedical images")
  Member "upsnet a unified panoptic segmentation network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep affinity net instance segmentation via affinity")
  Member "adaptis adaptive instance selection network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "knowledge distillation via adaptive instance normalization")
  Member "mask scoring r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "proposal learning for semi supervised object detection")
  Member "panoptic feature pyramid networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mti net multi scale task interaction networks for multi task learning")
  Member "attention guided unified network for panoptic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards bounding box free panoptic segmentation")
  Member "learning to fuse things and stuff":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")

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)