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: "item popularity prediction in e commerce using image quality feature vectors"
  Abstract: Online retail is a visual experience- Shoppers often use images as first order information to decide if an item matches their personal style. Image characteristics such as color, simplicity, scene composition, texture, style, aesthetics and overall quality play a crucial role in making a purchase decision, clicking on or liking a product listing. In this paper we use a set of image features that indicate quality to predict product listing popularity on a major e-commerce website, Etsy. We first define listing popularity through search clicks, favoriting and purchase activity. Next, we infer listing quality from the pixel-level information of listed images as quality features. We then compare our findings to text-only models for popularity prediction. Our initial results indicate that a combined image and text modeling of product listings outperforms text-only models in popularity prediction.

Connected through hyperedge HE-1 (induced by source paper "image matters scalable detection of offensive and non compliant content logo in product images"):
  with members:
    - "a smart system for selection of optimal product images in e commerce"
    - "learning transferable architectures for scalable image recognition"
    - "deep learning for logo recognition"
    - "ssd single shot multibox detector"
    - "deep learning logo detection with data expansion by synthesising context"
    - "r fcn object detection via region based fully convolutional networks"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "a smart system for selection of optimal product images in e commerce"):
  with members:
    - "learning transferable architectures for scalable image recognition"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "rethinking the inception architecture for computer vision"
    - "subjective and objective quality assessment of image a survey"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"):
  with members:
    - "time matters multi scale temporalization of social media popularity"
    - "science askscience and badscience on the coexistence of highly related communities"
    - "beyond exchangeability the chinese voting process"
    - "learning distributed representations of sentences from unlabelled data"
    - "understanding image virality"
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "deep residual learning for image recognition"
    - "an image is worth more than a thousand favorites surfacing the hidden beauty of flickr pictures"

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)