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: "mvtec d2s densely segmented supermarket dataset"
  Abstract: We introduce the Densely Segmented Supermarket (D2S) dataset, a novel benchmark for instance-aware semantic segmentation in an industrial domain. It contains 21,000 high-resolution images with pixel-wise labels of all object instances. The objects comprise groceries and everyday products from 60 categories. The benchmark is designed such that it resembles the real-world setting of an automatic checkout, inventory, or warehouse system. The training images only contain objects of a single class on a homogeneous background, while the validation and test sets are much more complex and diverse. To further benchmark the robustness of instance segmentation methods, the scenes are acquired with different lightings, rotations, and backgrounds. We ensure that there are no ambiguities in the labels and that every instance is labeled comprehensively. The annotations are pixel-precise and allow using crops of single instances for articial data augmentation. The dataset covers several challenges highly relevant in the field, such as a limited amount of training data and a high diversity in the test and validation sets. The evaluation of state-of-the-art object detection and instance segmentation methods on D2S reveals significant room for improvement.

Connected through hyperedge HE-1 (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"
    - "deep ordinal regression network for monocular depth estimation"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "style aggregated network for facial landmark detection"

Connected through hyperedge HE-2 (induced by source paper "rethinking object detection in retail stores"):
  with members:
    - "reppoints point set representation for object detection"
    - "data priming network for automatic check out"
    - "fcos fully convolutional one stage object detection"
    - "objects as points"
    - "rpc a large scale retail product checkout dataset"
    - "object counting and instance segmentation with image level supervision"
    - "cornernet detecting objects as paired keypoints"
    - "where are the blobs counting by localization with point supervision"

Connected through hyperedge HE-3 (induced by source paper "oriented boxes for accurate instance segmentation"):
  with members:
    - "retinamask learning to predict masks improves state of the art single shot detection for free"
    - "mask scoring r cnn"
    - "shapemask learning to segment novel objects by refining shape priors"
    - "acquire augment segment enjoy weakly supervised instance segmentation of supermarket products"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "path aggregation network for instance segmentation"

Connected through hyperedge HE-4 (induced by source paper "learning semantics aware distance map with semantics layering network for amodal instance segmentation"):
  with members:
    - "segan segmenting and generating the invisible"
    - "depth image inpainting improving low rank matrix completion with low gradient regularization"
    - "mask r cnn"
    - "pixel level encoding and depth layering for instance level semantic labeling"
    - "ssd single shot multibox detector"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "learning to refine object segments"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-5 (induced by source paper "data priming network for automatic check out"):
  with members:
    - "rpc a large scale retail product checkout dataset"
    - "deeply supervised salient object detection with short connections"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "visual domain adaptation with manifold embedded distribution alignment"
    - "asymmetric tri training for unsupervised domain adaptation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "salient object detection a discriminative regional feature integration approach"

Connected through hyperedge HE-6 (induced by source paper "benchmark for generic product detection a strong baseline for dense object detection"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "rpc a large scale retail product checkout dataset"
    - "product recognition in store shelves as a sub graph isomorphism problem"
    - "focal loss for dense object detection"
    - "incremental learning of object detectors without catastrophic forgetting"
    - "an analysis of scale invariance in object detection snip"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "microsoft coco common objects in context"

Connected through hyperedge HE-7 (induced by source paper "integration of text maps in convolutional neural networks for region detection among different textual categories"):
  with members:
    - "integrating scene text and visual appearance for fine grained image classification"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "multimodal deep learning for robust rgb d object recognition"
    - "adam a method for stochastic optimization"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "acquire augment segment enjoy weakly supervised instance segmentation of supermarket products"):
  with members:
    - "learning to segment every thing"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "simple does it weakly supervised instance and semantic segmentation"
    - "amodal instance segmentation"
    - "semantic amodal segmentation"
    - "inner and inter label propagation salient object detection in the wild"
    - "salient object detection a benchmark"
    - "imagenet large scale visual recognition challenge"

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)