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: "visual discovery at pinterest"
  Abstract: Over the past three years Pinterest has experimented with several visual search and recommendation services, including Related Pins (2014), Similar Looks (2015), Flashlight (2016) and Lens (2017). This paper presents an overview of our visual discovery engine powering these services, and shares the rationales behind our technical and product decisions such as the use of object detection and interactive user interfaces. We conclude that this visual discovery engine significantly improves engagement in both search and recommendation tasks.

Connected through hyperedge HE-1 (induced by source paper "handling position bias for unbiased learning to rank in hotels search"):
  with members:
    - "deep learning recommendation model for personalization and recommendation systems"
    - "estimating position bias without intrusive interventions"
    - "position bias estimation for unbiased learning to rank in ecommerce search"
    - "unbiased learning to rank with unbiased propensity estimation"
    - "applying deep learning to airbnb search"
    - "direct estimation of position bias for unbiased learning to rank without intervention"
    - "consistent position bias estimation without online interventions for learning to rank"
    - "related pins at pinterest the evolution of a real world recommender system"

Connected through hyperedge HE-2 (induced by source paper "learning a unified embedding for visual search at pinterest"):
  with members:
    - "classification is a strong baseline for deep metric learning"
    - "graph convolutional neural networks for web scale recommender systems"
    - "group normalization"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "taskonomy disentangling task transfer learning"
    - "web scale responsive visual search at bing"
    - "sampling matters in deep embedding learning"
    - "visual search at ebay"

Connected through hyperedge HE-3 (induced by source paper "end to end efficient representation learning via cascading combinatorial optimization"):
  with members:
    - "efficient end to end learning for quantizable representations"
    - "deep supervised discrete hashing"
    - "a downsampled variant of imagenet as an alternative to the cifar datasets"
    - "deep metric learning via facility location"
    - "a survey on learning to hash"
    - "hard aware deeply cascaded embedding"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "unsupervised learning of visual representations using videos"

Connected through hyperedge HE-4 (induced by source paper "user preference prediction in visual data on mobile devices"):
  with members:
    - "efficient facial representations for age gender and identity recognition in organizing photo albums using multi output convnet"
    - "unconstrained face identification using maximum likelihood of distances between deep off the shelf features"
    - "deep learning based large scale visual recommendation and search for e commerce"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learnable pooling with context gating for video classification"
    - "neural aggregation network for video face recognition"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "speed accuracy trade offs for modern convolutional object detectors"

Connected through hyperedge HE-5 (induced by source paper "applications of generative adversarial models in visual search reformulation"):
  with members:
    - "a style based generator architecture for generative adversarial networks"
    - "web scale responsive visual search at bing"
    - "attgan facial attribute editing by only changing what you want"
    - "triangle generative adversarial networks"
    - "cycada cycle consistent adversarial domain adaptation"
    - "visual search at ebay"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-6 (induced by source paper "visual search over billions of aerial and satellite images"):
  with members:
    - "generalization in metric learning should the embedding layer be the embedding layer"
    - "good practice in cnn feature transfer"
    - "sift meets cnn a decade survey of instance retrieval"
    - "data intensive supercomputing in the cloud global analytics for satellite imagery"
    - "deep residual learning for image recognition"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "visual search at pinterest"
    - "neural codes for image retrieval"

Connected through hyperedge HE-7 (induced by source paper "complete the look scene based complementary product recommendation"):
  with members:
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "do better imagenet models transfer better"
    - "video2shop exactly matching clothes in videos to online shopping images"
    - "learning type aware embeddings for fashion compatibility"
    - "recommending outfits from personal closet"
    - "creating capsule wardrobes from fashion images"
    - "automatic spatially aware fashion concept discovery"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "classification is a strong baseline for deep metric learning"):
  with members:
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "deep randomized ensembles for metric learning"
    - "deep metric learning with hierarchical triplet loss"
    - "improving generalization via scalable neighborhood component analysis"
    - "web scale responsive visual search at bing"
    - "sampling matters in deep embedding learning"
    - "no fuss distance metric learning using proxies"
    - "deep metric learning via facility location"

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)