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: "rpc a large scale retail product checkout dataset"
  Abstract: Over recent years, emerging interest has occurred in integrating computer vision technology into the retail industry. Automatic checkout (ACO) is one of the critical problems in this area which aims to automatically generate the shopping list from the images of the products to purchase. The main challenge of this problem comes from the large scale and the fine-grained nature of the product categories as well as the difficulty for collecting training images that reflect the realistic checkout scenarios due to continuous update of the products. Despite its significant practical and research value, this problem is not extensively studied in the computer vision community, largely due to the lack of a high-quality dataset. To fill this gap, in this work we propose a new dataset to facilitate relevant research. Our dataset enjoys the following characteristics: (1) It is by far the largest dataset in terms of both product image quantity and product categories. (2) It includes single-product images taken in a controlled environment and multi-product images taken by the checkout system. (3) It provides different levels of annotations for the check-out images. Comparing with the existing datasets, ours is closer to the realistic setting and can derive a variety of research problems. Besides the dataset, we also benchmark the performance on this dataset with various approaches. The dataset and related resources can be found at \url{this https URL}.

Connected through hyperedge HE-1 (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"
    - "object counting and instance segmentation with image level supervision"
    - "cornernet detecting objects as paired keypoints"
    - "mvtec d2s densely segmented supermarket dataset"
    - "where are the blobs counting by localization with point supervision"

Connected through hyperedge HE-2 (induced by source paper "multi label image recognition with graph convolutional networks"):
  with members:
    - "chest x rays classification a multi label and fine grained problem"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "multi label zero shot learning with structured knowledge graphs"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "fasttext zip compressing text classification models"

Connected through hyperedge HE-3 (induced by source paper "deep learning for fine grained image analysis a survey"):
  with members:
    - "piecewise classifier mappings learning fine grained learners for novel categories with few examples"
    - "fine grained visual categorization using meta learning optimization with sample selection of auxiliary data"
    - "multi attention multi class constraint for fine grained image recognition"
    - "maximum entropy fine grained classification"
    - "learning to navigate for fine grained classification"
    - "coarse to fine a rnn based hierarchical attention model for vehicle re identification"
    - "part aligned bilinear representations for person re identification"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "product image recognition with guidance learning and noisy supervision"):
  with members:
    - "deep learning is robust to massive label noise"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "joint optimization framework for learning with noisy labels"
    - "cleannet transfer learning for scalable image classifier training with label noise"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "webvision database visual learning and understanding from web data"
    - "learning from noisy large scale datasets with minimal supervision"
    - "learning from noisy labels with distillation"

Connected through hyperedge HE-5 (induced by source paper "data priming network for automatic check out"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "mvtec d2s densely segmented supermarket dataset"
    - "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 "bbn bilateral branch network with cumulative learning for long tailed visual recognition"):
  with members:
    - "lvis a dataset for large vocabulary instance segmentation"
    - "large scale long tailed recognition in an open world"
    - "class balanced loss based on effective number of samples"
    - "deep learning for fine grained image analysis a survey"
    - "what is the effect of importance weighting in deep learning"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "the devil is in the tails fine grained classification in the wild"
    - "focal loss for dense object detection"

Connected through hyperedge HE-7 (induced by source paper "benchmark for generic product detection a strong baseline for dense object detection"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "mvtec d2s densely segmented supermarket 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"

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)