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: "panoptic segmentation"
  Abstract: We propose and study a task we name panoptic segmentation (PS). Panoptic segmentation unifies the typically distinct tasks of semantic segmentation (assign a class label to each pixel) and instance segmentation (detect and segment each object instance). The proposed task requires generating a coherent scene segmentation that is rich and complete, an important step toward real-world vision systems. While early work in computer vision addressed related image/scene parsing tasks, these are not currently popular, possibly due to lack of appropriate metrics or associated recognition challenges. To address this, we propose a novel panoptic quality (PQ) metric that captures performance for all classes (stuff and things) in an interpretable and unified manner. Using the proposed metric, we perform a rigorous study of both human and machine performance for PS on three existing datasets, revealing interesting insights about the task. The aim of our work is to revive the interest of the community in a more unified view of image segmentation.

Connected through hyperedge HE-1 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans"):
  with members:
    - "teaser fast and certifiable point cloud registration"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "in perfect shape certifiably optimal 3d shape reconstruction from 2d landmarks"
    - "semi supervised semantic mapping through label propagation with semantic texture meshes"
    - "kimera an open source library for real time metric semantic localization and mapping"
    - "3 d scene graph a sparse and semantic representation of physical environments for intelligent agents"
    - "value iteration networks on multiple levels of abstraction"
    - "incremental visual inertial 3d mesh generation with structural regularities"

Connected through hyperedge HE-2 (induced by source paper "d3vo deep depth deep pose and deep uncertainty for monocular visual odometry"):
  with members:
    - "visual inertial mapping with non linear factor recovery"
    - "a general optimization based framework for local odometry estimation with multiple sensors"
    - "multi frame gan image enhancement for stereo visual odometry in low light"
    - "unsupervised scale consistent depth and ego motion learning from monocular video"
    - "visual odometry revisited what should be learnt"
    - "deeptam deep tracking and mapping"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "codeslam learning a compact optimisable representation for dense visual slam"

Connected through hyperedge HE-3 (induced by source paper "towards bounding box free panoptic segmentation"):
  with members:
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "panoptic edge detection"
    - "upsnet a unified panoptic segmentation network"
    - "fast panoptic segmentation network"
    - "adaptis adaptive instance selection network"
    - "ssap single shot instance segmentation with affinity pyramid"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "panoptic feature pyramid networks"

Connected through hyperedge HE-4 (induced by source paper "cell r cnn v3 a novel panoptic paradigm for instance segmentation in biomedical images"):
  with members:
    - "an end to end network for 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-5 (induced by source paper "multi task learning by a top down control network"):
  with members:
    - "clevr ref diagnosing visual reasoning with referring expressions"
    - "superposition of many models into one"
    - "many task learning with task routing"
    - "attentive single tasking of multiple tasks"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "top down feedback for crowd counting convolutional neural network"
    - "multi task learning as multi objective optimization"
    - "progressive neural networks"

Connected through hyperedge HE-6 (induced by source paper "deepsemantichppc hypothesis based planning over uncertain semantic point clouds"):
  with members:
    - "learn to score efficient 3d scene exploration by predicting view utility"
    - "surface edge explorer see planning next best views directly from 3d observations"
    - "dynamic graph cnn for learning on point clouds"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "learning ground traversability from simulations"
    - "semantic3d net a new large scale point cloud classification benchmark"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "frustum pointnets for 3d object detection from rgb d data"

Connected through hyperedge HE-7 (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"
    - "an end to end network for 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-8 (induced by source paper "deeperlab single shot image parser"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "affinity derivation and graph merge for instance segmentation"
    - "unified perceptual parsing for scene understanding"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "path aggregation network for instance segmentation"
    - "large kernel matters improve semantic segmentation by global convolutional network"

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)