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: "an end to end network for panoptic segmentation"
  Abstract: Panoptic segmentation, which needs to assign a category label to each pixel and segment each object instance simultaneously, is a challenging topic. Traditionally, the existing approaches utilize two independent models without sharing features, which makes the pipeline inefficient to implement. In addition, a heuristic method is usually employed to merge the results. However, the overlapping relationship between object instances is difficult to determine without sufficient context information during the merging process. To address the problems, we propose a novel end-to-end network for panoptic segmentation, which can efficiently and effectively predict both the instance and stuff segmentation in a single network. Moreover, we introduce a novel spatial ranking module to deal with the occlusion problem between the predicted instances. Extensive experiments have been done to validate the performance of our proposed method and promising results have been achieved on the COCO Panoptic benchmark.

Connected through hyperedge HE-1 (induced by source paper "cell r cnn v3 a novel panoptic paradigm for instance segmentation in biomedical images"):
  with members:
    - "panoptic segmentation"
    - "data augmentation for leaf segmentation and counting tasks in rosette plants"
    - "mask scoring r cnn"
    - "instance segmentation of biological images using harmonic embeddings"
    - "panoptic feature pyramid networks"
    - "cascade r cnn high quality object detection and instance segmentation"
    - "deep leaf segmentation using synthetic data"
    - "group normalization"

Connected through hyperedge HE-2 (induced by source paper "a benchmark for lidar based panoptic segmentation based on kitti"):
  with members:
    - "jsis3d joint semantic instance segmentation of 3d point clouds with multi task pointwise networks and multi value conditional random fields"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "panoptic segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "scalability in perception for autonomous driving waymo open dataset"
    - "seamless scene segmentation"
    - "kpconv flexible and deformable convolution for point clouds"
    - "nuscenes a multimodal dataset for autonomous driving"

Connected through hyperedge HE-3 (induced by source paper "seamless scene segmentation"):
  with members:
    - "upsnet a unified panoptic segmentation network"
    - "deeperlab single shot image parser"
    - "panoptic feature pyramid networks"
    - "learning to fuse things and stuff"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "path aggregation network for instance segmentation"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "idd a dataset for exploring problems of autonomous navigation in unconstrained environments"

Connected through hyperedge HE-4 (induced by source paper "spatialflow bridging all tasks for panoptic segmentation"):
  with members:
    - "panoptic segmentation"
    - "retinamask learning to predict masks improves state of the art single shot detection for free"
    - "region proposal by guided anchoring"
    - "upsnet a unified panoptic segmentation network"
    - "box driven class wise region masking and filling rate guided loss for weakly supervised semantic segmentation"
    - "deeperlab single shot image parser"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "hybrid task cascade for instance segmentation"

Connected through hyperedge HE-5 (induced by source paper "sognet scene overlap graph network for panoptic segmentation"):
  with members:
    - "learning instance occlusion for panoptic segmentation"
    - "expectation maximization attention networks for semantic 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-6 (induced by source paper "learning instance occlusion for panoptic segmentation"):
  with members:
    - "panoptic segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "adaptis adaptive instance selection network"
    - "mask scoring r cnn"
    - "panoptic feature pyramid networks"
    - "attention guided unified network for panoptic segmentation"
    - "learning to fuse things and stuff"
    - "panoptic segmentation with a joint semantic and instance segmentation network"

Connected through hyperedge HE-7 (induced by source paper "single shot panoptic segmentation"):
  with members:
    - "panoptic segmentation"
    - "learning instance occlusion 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-8 (induced by source paper "bipartite conditional random fields for panoptic segmentation"):
  with members:
    - "panoptic segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "seamless scene segmentation"
    - "deeperlab single shot image parser"
    - "ssap single shot instance segmentation with affinity pyramid"
    - "panoptic feature pyramid networks"
    - "attention guided unified network for panoptic segmentation"
    - "path aggregation network for instance segmentation"

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)