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: "deep ctr prediction in display advertising"
  Abstract: Click through rate (CTR) prediction of image ads is the core task of online display advertising systems, and logistic regression (LR) has been frequently applied as the prediction model. However, LR model lacks the ability of extracting complex and intrinsic nonlinear features from handcrafted high-dimensional image features, which limits its effectiveness. To solve this issue, in this paper, we introduce a novel deep neural network (DNN) based model that directly predicts the CTR of an image ad based on raw image pixels and other basic features in one step. The DNN model employs convolution layers to automatically extract representative visual features from images, and nonlinear CTR features are then learned from visual features and other contextual features by using fully-connected layers. Empirical evaluations on a real world dataset with over 50 million records demonstrate the effectiveness and efficiency of this method.

Connected through hyperedge HE-1 (induced by source paper "targeted display advertising the case of preferential attachment"):
  with members:
    - "adaptive matrix completion for the users and the items in tail"
    - "intent term selection and refinement in e commerce queries"
    - "deep visual domain adaptation a survey"
    - "field weighted factorization machines for click through rate prediction in display advertising"
    - "unified deep supervised domain adaptation and generalization"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "deepfm a factorization machine based neural network for ctr prediction"

Connected through hyperedge HE-2 (induced by source paper "feature generation by convolutional neural network for click through rate prediction"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "deepfm an end to end wide deep learning framework for ctr prediction"
    - "product based neural networks for user response prediction over multi field categorical data"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "learning to generate reviews and discovering sentiment"
    - "neural factorization machines for sparse predictive analytics"
    - "attention is all you need"
    - "failures of gradient based deep learning"

Connected through hyperedge HE-3 (induced by source paper "which ads to show advertisement image assessment with auxiliary information via multi step modality fusion"):
  with members:
    - "what is wrong with scene text recognition model comparisons dataset and model analysis"
    - "character region awareness for text detection"
    - "multimodal dual attention memory for video story question answering"
    - "nima neural image assessment"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "aesthetic based clothing recommendation"
    - "nsml meet the mlaas platform with a real world case study"

Connected through hyperedge HE-4 (induced by source paper "conversion prediction using multi task conditional attention networks to support the creation of effective ad creatives"):
  with members:
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "field aware factorization machines in a real world online advertising system"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "a structured self attentive sentence embedding"
    - "wide deep learning for recommender systems"
    - "multi task sequence to sequence learning"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-5 (induced by source paper "enabling hyper personalisation automated ad creative generation and ranking for fashion e commerce"):
  with members:
    - "layoutgan generating graphic layouts with wireframe discriminators"
    - "nima neural image assessment"
    - "mask r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "exploring online ad images using a deep convolutional neural network approach"
    - "return of the devil in the details delving deep into convolutional nets"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "conflict as an inverse of attention in sequence relationship"):
  with members:
    - "deep contextualized word representations"
    - "stochastic answer networks for natural language inference"
    - "attention is all you need"
    - "neural paraphrase identification of questions with noisy pretraining"
    - "bilateral multi perspective matching for natural language sentences"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (induced by source paper "an enhanced ad event prediction method based on feature engineering"):
  with members:
    - "click through rate prediction for contextual advertisment using linear regression"
    - "deep learning over multi field categorical data a case study on user response prediction"
    - "wide deep learning for recommender systems"
    - "a review of feature selection methods based on mutual information"
    - "sequential click prediction for sponsored search with recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "deepfm an end to end wide deep learning framework for ctr prediction"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "deep learning over multi field categorical data a case study on user response prediction"
    - "wide deep learning for recommender systems"
    - "product based neural networks for user response prediction"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "deep residual learning for image recognition"

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)