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: "images don t lie transferring deep visual semantic features to large scale multimodal learning to rank"
  Abstract: Search is at the heart of modern e-commerce. As a result, the task of ranking search results automatically (learning to rank) is a multibillion dollar machine learning problem. Traditional models optimize over a few hand-constructed features based on the item's text. In this paper, we introduce a multimodal learning to rank model that combines these traditional features with visual semantic features transferred from a deep convolutional neural network. In a large scale experiment using data from the online marketplace Etsy, we verify that moving to a multimodal representation significantly improves ranking quality. We show how image features can capture fine-grained style information not available in a text-only representation. In addition, we show concrete examples of how image information can successfully disentangle pairs of highly different items that are ranked similarly by a text-only model.

Connected through hyperedge HE-1 (induced by source paper "production ranking systems a review"):
  with members:
    - "beyond keywords and relevance a personalized ad retrieval framework in e commerce sponsored search"
    - "practical constrained optimization of auction mechanisms in e commerce sponsored search advertising"
    - "eenmf an end to end neural matching framework for e commerce sponsored search"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "visually aware fashion recommendation and design with generative image models"
    - "pixie a system for recommending 3 billion items to 200 million users in real time"

Connected through hyperedge HE-2 (induced by source paper "dynamic deep multi modal fusion for image privacy prediction"):
  with members:
    - "towards a visual privacy advisor understanding and predicting privacy risks in images"
    - "places an image database for deep scene understanding"
    - "convolutional two stream network fusion for video action recognition"
    - "is a picture worth a thousand words a deep multi modal fusion architecture for product classification in e commerce"
    - "object scene convolutional neural networks for event recognition in images"
    - "meta des"
    - "very deep convolutional networks for large scale image recognition"
    - "distributed representations of sentences and documents"

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"
    - "item popularity prediction in e commerce using image quality feature vectors"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "amc attention guided multi modal correlation learning for image search"):
  with members:
    - "leveraging visual question answering for image caption ranking"
    - "deep image retrieval learning global representations for image search"
    - "hierarchical question image co attention for visual question answering"
    - "dynamic memory networks for visual and textual question answering"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "learning attributes equals multi source domain generalization"
    - "deep semantic ranking based hashing for multi label image retrieval"

Connected through hyperedge HE-5 (induced by source paper "image matters visually modeling user behaviors using advanced model server"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "attention is all you need"
    - "neural collaborative filtering"
    - "optimized cost per click in taobao display advertising"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "wide deep learning for recommender systems"
    - "deep ctr prediction in display advertising"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "an ensemble based approach to click through rate prediction for promoted listings at etsy"):
  with members:
    - "deep residual learning for image recognition"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-7 (induced by source paper "is a picture worth a thousand words a deep multi modal fusion architecture for product classification in e commerce"):
  with members:
    - "very deep convolutional networks for text classification"
    - "efficient character level document classification by combining convolution and recurrent layers"
    - "deep residual learning for image recognition"
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "convolutional neural networks for sentence classification"
    - "intriguing properties of neural 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)