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 search at ebay"
  Abstract: In this paper, we propose a novel end-to-end approach for scalable visual search infrastructure. We discuss the challenges we faced for a massive volatile inventory like at eBay and present our solution to overcome those. We harness the availability of large image collection of eBay listings and state-of-the-art deep learning techniques to perform visual search at scale. Supervised approach for optimized search limited to top predicted categories and also for compact binary signature are key to scale up without compromising accuracy and precision. Both use a common deep neural network requiring only a single forward inference. The system architecture is presented with in-depth discussions of its basic components and optimizations for a trade-off between search relevance and latency. This solution is currently deployed in a distributed cloud infrastructure and fuels visual search in eBay ShopBot and Close5. We show benchmark on ImageNet dataset on which our approach is faster and more accurate than several unsupervised baselines. We share our learnings with the hope that visual search becomes a first class citizen for all large scale search engines rather than an afterthought.

Connected through hyperedge HE-1 (induced by source paper "unsupervised adversarial attacks on deep feature based retrieval with gan"):
  with members:
    - "learning discriminative features with multiple granularities for person re identification"
    - "generating adversarial examples with adversarial networks"
    - "sphereface deep hypersphere embedding for face recognition"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "attacking visual language grounding with adversarial examples a case study on neural image captioning"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "fine tuning cnn image retrieval with no human annotation"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-2 (induced by source paper "pose guided fashion image synthesis using deep generative model"):
  with members:
    - "a variational u net for conditional appearance and shape generation"
    - "everybody dance now"
    - "viton an image based virtual try on network"
    - "pose guided person image generation"
    - "looking at outfit to parse clothing"
    - "a generative model of people in clothing"
    - "be your own prada fashion synthesis with structural coherence"
    - "towards accurate multi person pose estimation in the wild"

Connected through hyperedge HE-3 (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"
    - "squeeze and excitation networks"

Connected through hyperedge HE-4 (induced by source paper "seeker real time interactive search"):
  with members:
    - "building a conversational agent overnight with dialogue self play"
    - "deep contextualized word representations"
    - "diversifying music recommendations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "web scale responsive visual search at bing"
    - "flowqa grasping flow in history for conversational machine comprehension"
    - "boltzmann exploration done right"
    - "a tutorial on thompson sampling"

Connected through hyperedge HE-5 (induced by source paper "large scale landmark recognition via deep metric learning"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "detect to retrieve efficient regional aggregation for image search"
    - "billion scale similarity search with gpus"
    - "squeeze and excitation networks"
    - "fine tuning cnn image retrieval with no human annotation"
    - "end to end learning of deep visual representations for image retrieval"
    - "large scale image retrieval with attentive deep local features"

Connected through hyperedge HE-6 (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"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "visual discovery at pinterest"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-7 (induced by source paper "texture retrieval in the wild through detection based attributes"):
  with members:
    - "texel att representing and classifying element based textures by attributes"
    - "a survey of recent advances in texture representation"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "mask r cnn"
    - "whittlesearch interactive image search with relative attribute feedback"
    - "deep filter banks for texture recognition description and segmentation"

Connected through hyperedge HE-8 (induced by source paper "xcloud design and implementation of ai cloud platform with restful api service"):
  with members:
    - "data driven approach for quality evaluation on knowledge sharing platform"
    - "scut fbp5500 a diverse benchmark dataset for multi paradigm facial beauty prediction"
    - "sphereface deep hypersphere embedding for face recognition"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

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)