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: "seamless scene segmentation"
  Abstract: In this work we introduce a novel, CNN-based architecture that can be trained end-to-end to deliver seamless scene segmentation results. Our goal is to predict consistent semantic segmentation and detection results by means of a panoptic output format, going beyond the simple combination of independently trained segmentation and detection models. The proposed architecture takes advantage of a novel segmentation head that seamlessly integrates multi-scale features generated by a Feature Pyramid Network with contextual information conveyed by a light-weight DeepLab-like module. As additional contribution we review the panoptic metric and propose an alternative that overcomes its limitations when evaluating non-instance categories. Our proposed network architecture yields state-of-the-art results on three challenging street-level datasets, i.e. Cityscapes, Indian Driving Dataset and Mapillary Vistas.

Connected through hyperedge HE-1 (induced by source paper "semantically guided representation learning for self supervised monocular depth"):
  with members:
    - "sdnet semantically guided depth estimation network"
    - "3d packing for self supervised monocular depth estimation"
    - "upsnet a unified panoptic segmentation network"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "panoptic feature pyramid networks"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "group normalization"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"

Connected through hyperedge HE-2 (induced by source paper "unifying training and inference for panoptic segmentation"):
  with members:
    - "deeperlab single shot image parser"
    - "panoptic deeplab a simple strong and fast baseline for bottom up panoptic segmentation"
    - "panoptic feature pyramid networks"
    - "ocnet object context network for scene parsing"
    - "learning to fuse things and stuff"
    - "dual attention network for scene segmentation"
    - "non local neural networks"
    - "attention is all you need"

Connected through hyperedge HE-3 (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"
    - "an end to end network for panoptic segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "scalability in perception for autonomous driving waymo open dataset"
    - "kpconv flexible and deformable convolution for point clouds"
    - "nuscenes a multimodal dataset for autonomous driving"

Connected through hyperedge HE-4 (induced by source paper "spgnet semantic prediction guidance for scene parsing"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "deeperlab single shot image parser"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "spg net segmentation prediction and guidance network for image inpainting"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "multi scale structure aware network for human pose estimation"

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"
    - "an end to end network for panoptic 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 multi object tracking and segmentation from automatic annotations"):
  with members:
    - "video instance segmentation"
    - "disentangling monocular 3d object detection"
    - "mots multi object tracking and segmentation"
    - "panoptic deeplab"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "predicting future instance segmentations by forecasting convolutional features"
    - "combined image and world space tracking in traffic scenes"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-7 (induced by source paper "bipartite conditional random fields for panoptic segmentation"):
  with members:
    - "panoptic segmentation"
    - "an end to end network for panoptic segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "real time panoptic segmentation from dense detections"):
  with members:
    - "panoptic segmentation"
    - "an end to end network for panoptic segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "fast panoptic segmentation network"
    - "adaptis adaptive instance selection network"
    - "polarmask single shot instance segmentation with polar representation"
    - "deeperlab single shot image parser"
    - "mask scoring r cnn"

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)