human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "sognet scene overlap graph network for panoptic segmentation"):
  with members:
    - "expectation maximization attention networks for semantic segmentation"
    - "an end to end network for panoptic segmentation"
    - "seamless scene segmentation"
    - "deeperlab single shot image parser"
    - "panoptic feature pyramid networks"
    - "bilinear attention networks"
    - "linknet relational embedding for scene graph"
    - "convolutional neural networks with alternately updated clique"

Connected through hyperedge HE-2 (induced by source paper "single shot panoptic segmentation"):
  with members:
    - "panoptic segmentation"
    - "an end to end network for panoptic segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "adaptis adaptive instance selection network"
    - "deeperlab single shot image parser"
    - "panoptic feature pyramid networks"
    - "cornernet detecting objects as paired keypoints"
    - "group normalization"

Connected through hyperedge HE-3 (induced by source paper "generator evaluator selector net a modular approach for panoptic segmentation"):
  with members:
    - "panoptic segmentation"
    - "an end to end network for panoptic segmentation"
    - "a review of modularization techniques in artificial neural networks"
    - "upsnet a unified panoptic segmentation network"
    - "selectivenet a deep neural network with an integrated reject option"
    - "class independent sequential full image segmentation using a convolutional net that finds a segment within an attention region given a pointer pixel within this segment"
    - "panoptic feature pyramid networks"
    - "classifying a specific image region using convolutional nets with an roi mask as input"

Question: Please tell me which class the center node 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)