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: "product recognition in store shelves as a sub graph isomorphism problem"
  Abstract: The arrangement of products in store shelves is carefully planned to maximize sales and keep customers happy. However, verifying compliance of real shelves to the ideal layout is a costly task routinely performed by the store personnel. In this paper, we propose a computer vision pipeline to recognize products on shelves and verify compliance to the planned layout. We deploy local invariant features together with a novel formulation of the product recognition problem as a sub-graph isomorphism between the items appearing in the given image and the ideal layout. This allows for auto-localizing the given image within the aisle or store and improving recognition dramatically.

Connected through hyperedge HE-1 (induced by source paper "bag of tricks for retail product image classification"):
  with members:
    - "benchmark for generic product detection a strong baseline for dense object detection"
    - "robustness properties of facebook s resnext wsl models"
    - "domain invariant hierarchical embedding for grocery products recognition"
    - "pytorch an imperative style high performance deep learning library"
    - "maximum entropy fine grained classification"
    - "exploring the limits of weakly supervised pretraining"
    - "superpoint self supervised interest point detection and description"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "domain invariant hierarchical embedding for grocery products recognition"):
  with members:
    - "deep semantic hashing with generative adversarial networks"
    - "sampling matters in deep embedding learning"
    - "deep learning based large scale visual recommendation and search for e commerce"
    - "prototypical networks for few shot learning"
    - "adversarial discriminative domain adaptation"
    - "learning spread out local feature descriptors"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "scalenet guiding object proposal generation in supermarkets and beyond"

Connected through hyperedge HE-3 (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"
    - "mvtec d2s densely segmented supermarket dataset"
    - "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-4 (induced by source paper "weakly supervised object localization on grocery shelves using simple fcn and synthetic dataset"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep self taught learning for weakly supervised object localization"
    - "synthesizing training data for object detection in indoor scenes"
    - "training and testing object detectors with virtual images"
    - "hide and seek forcing a network to be meticulous for weakly supervised object and action localization"
    - "deep learning logo detection with data expansion by synthesising context"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "a deep learning pipeline for product recognition on store shelves"):
  with members:
    - "deep learning based large scale visual recommendation and search for e commerce"
    - "scalenet guiding object proposal generation in supermarkets and beyond"
    - "deep image retrieval learning global representations for image search"
    - "yolo9000 better faster stronger"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "facenet a unified embedding for face recognition and clustering"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "faster r cnn towards real time object detection with region proposal networks"

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)