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 learning based large scale visual recommendation and search for e commerce"
  Abstract: In this paper, we present a unified end-to-end approach to build a large scale Visual Search and Recommendation system for e-commerce. Previous works have targeted these problems in isolation. We believe a more effective and elegant solution could be obtained by tackling them together. We propose a unified Deep Convolutional Neural Network architecture, called VisNet, to learn embeddings to capture the notion of visual similarity, across several semantic granularities. We demonstrate the superiority of our approach for the task of image retrieval, by comparing against the state-of-the-art on the Exact Street2Shop dataset. We then share the design decisions and trade-offs made while deploying the model to power Visual Recommendations across a catalog of 50M products, supporting 2K queries a second at Flipkart, India's largest e-commerce company. The deployment of our solution has yielded a significant business impact, as measured by the conversion-rate.

Connected through hyperedge HE-1 (induced by source paper "featurenms non maximum suppression by learning feature embeddings"):
  with members:
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "large batch optimization for deep learning training bert in 76 minutes"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "yolov3 an incremental improvement"
    - "crowdhuman a benchmark for detecting human in a crowd"
    - "sampling matters in deep embedding learning"
    - "soft nms improving object detection with one line of code"
    - "focal loss for dense object detection"

Connected through hyperedge HE-2 (induced by source paper "fine grained fashion similarity learning by attribute specific embedding network"):
  with members:
    - "learning type aware embeddings for fashion compatibility"
    - "cbam convolutional block attention module"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "clothing retrieval with visual attention model"
    - "attention is all you need"
    - "automatic spatially aware fashion concept discovery"
    - "learning fashion compatibility with bidirectional lstms"
    - "exploring human like attention supervision in visual question answering"

Connected through hyperedge HE-3 (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"
    - "product recognition in store shelves as a sub graph isomorphism problem"
    - "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-4 (induced by source paper "semantic granularity metric learning for visual search"):
  with members:
    - "generalization in metric learning should the embedding layer be the embedding layer"
    - "deep randomized ensembles for metric learning"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "deep metric learning with hierarchical triplet loss"
    - "in defense of the triplet loss for person re identification"
    - "local similarity aware deep feature embedding"
    - "hard aware deeply cascaded embedding"
    - "simultaneous feature learning and hash coding with deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "the resale price prediction of secondhand jewelry items using a multi modal deep model with iterative co attention"):
  with members:
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "murel multimodal relational reasoning for visual question answering"
    - "deepstyle multimodal search engine for fashion and interior design"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "beyond bilinear generalized multi modal factorized high order pooling for visual question answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"
    - "hadamard product for low rank bilinear pooling"

Connected through hyperedge HE-6 (induced by source paper "searching for apparel products from images in the wild"):
  with members:
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "in defense of the triplet loss for person re identification"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "looking at outfit to parse clothing"
    - "ssd single shot multibox detector"
    - "speed accuracy trade offs for modern convolutional object detectors"

Connected through hyperedge HE-7 (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"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learnable pooling with context gating for video classification"
    - "visual discovery at pinterest"
    - "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-8 (induced by source paper "visually explainable recommendation"):
  with members:
    - "neural collaborative filtering"
    - "neural rating regression with abstractive tips generation for recommendation"
    - "explainable recommendation theory and applications"
    - "a visual and textual recurrent neural network for sequential prediction"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "interpretable recommendations via overlapping co clusters"
    - "explaining reviews and ratings with paco poisson additive co clustering"
    - "vbpr visual bayesian personalized ranking from implicit feedback"

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)