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: "coco stuff thing and stuff classes in context"
  Abstract: Semantic classes can be either things (objects with a well-defined shape, e.g. car, person) or stuff (amorphous background regions, e.g. grass, sky). While lots of classification and detection works focus on thing classes, less attention has been given to stuff classes. Nonetheless, stuff classes are important as they allow to explain important aspects of an image, including (1) scene type; (2) which thing classes are likely to be present and their location (through contextual reasoning); (3) physical attributes, material types and geometric properties of the scene. To understand stuff and things in context we introduce COCO-Stuff, which augments all 164K images of the COCO 2017 dataset with pixel-wise annotations for 91 stuff classes. We introduce an efficient stuff annotation protocol based on superpixels, which leverages the original thing annotations. We quantify the speed versus quality trade-off of our protocol and explore the relation between annotation time and boundary complexity. Furthermore, we use COCO-Stuff to analyze: (a) the importance of stuff and thing classes in terms of their surface cover and how frequently they are mentioned in image captions; (b) the spatial relations between stuff and things, highlighting the rich contextual relations that make our dataset unique; (c) the performance of a modern semantic segmentation method on stuff and thing classes, and whether stuff is easier to segment than things.

Connected through hyperedge HE-1 (induced by source paper "object centric image generation from layouts"):
  with members:
    - "interactive image generation using scene graphs"
    - "scene graph generation with external knowledge and image reconstruction"
    - "object driven text to image synthesis via adversarial training"
    - "image synthesis from reconfigurable layout and style"
    - "pastegan a semi parametric method to generate image from scene graph"
    - "locality and compositionality in zero shot learning"
    - "semantic image synthesis with spatially adaptive normalization"
    - "semantics disentangling for text to image generation"

Connected through hyperedge HE-2 (induced by source paper "deep multicameral decoding for localizing unoccluded object instances from a single rgb image"):
  with members:
    - "fully dense unet for 2 d sparse photoacoustic tomography artifact removal"
    - "dual graph convolutional network for semantic segmentation"
    - "pointrend image segmentation as rendering"
    - "quantized densely connected u nets for efficient landmark localization"
    - "object segmentation in depth maps with one user click and a synthetically trained fully convolutional network"
    - "mvtec d2s densely segmented supermarket dataset"
    - "deep ordinal regression network for monocular depth estimation"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"

Connected through hyperedge HE-3 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "interactive full image segmentation by considering all regions jointly"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning to segment via cut and paste"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "visual affordance and function understanding a survey"

Connected through hyperedge HE-4 (induced by source paper "deepsemantichppc hypothesis based planning over uncertain semantic point clouds"):
  with members:
    - "panoptic segmentation"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "invisible marker automatic annotation of segmentation masks for object manipulation"):
  with members:
    - "affordance learning for end to end visuomotor robot control"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "fluid annotation a human machine collaboration interface for full image annotation"
    - "interactively picking real world objects with unconstrained spoken language instructions"
    - "recent trends in deep learning based natural language processing"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "detection and tracking of liquids with fully convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-6 (induced by source paper "visual semantic information pursuit a survey"):
  with members:
    - "linknet relational embedding for scene graph"
    - "iterative visual reasoning beyond convolutions"
    - "graph r cnn for scene graph generation"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "large scale visual relationship understanding"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "visual relationship detection based on guided proposals and semantic knowledge distillation"
    - "factorizable net an efficient subgraph based framework for scene graph generation"

Connected through hyperedge HE-7 (induced by source paper "learning canonical representations for scene graph to image generation"):
  with members:
    - "spatio temporal action graph networks"
    - "interactive image generation using scene graphs"
    - "image synthesis from reconfigurable layout and style"
    - "on the universality of invariant networks"
    - "mirrorgan learning text to image generation by redescription"
    - "text guided person image synthesis"
    - "semantic image synthesis with spatially adaptive normalization"
    - "semantics disentangling for text to image generation"

Connected through hyperedge HE-8 (induced by source paper "panoptic feature pyramid networks"):
  with members:
    - "panoptic segmentation"
    - "group normalization"
    - "learning to fuse things and stuff"
    - "panoptic segmentation with a joint semantic and instance segmentation network"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "path aggregation network for instance segmentation"
    - "weakly and semi supervised panoptic segmentation"
    - "triply supervised decoder networks for joint detection and 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)