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: "web scale responsive visual search at bing"
  Abstract: In this paper, we introduce a web-scale general visual search system deployed in Microsoft Bing. The system accommodates tens of billions of images in the index, with thousands of features for each image, and can respond in less than 200 ms. In order to overcome the challenges in relevance, latency, and scalability in such large scale of data, we employ a cascaded learning-to-rank framework based on various latest deep learning visual features, and deploy in a distributed heterogeneous computing platform. Quantitative and qualitative experiments show that our system is able to support various applications on Bing website and apps.

Connected through hyperedge HE-1 (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"
    - "sampling matters in deep embedding learning"
    - "visual search at ebay"
    - "squeeze and excitation networks"

Connected through hyperedge HE-2 (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"
    - "flowqa grasping flow in history for conversational machine comprehension"
    - "boltzmann exploration done right"
    - "a tutorial on thompson sampling"
    - "visual search at ebay"

Connected through hyperedge HE-3 (induced by source paper "applications of generative adversarial models in visual search reformulation"):
  with members:
    - "a style based generator architecture for generative adversarial networks"
    - "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"
    - "visual discovery at pinterest"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-4 (induced by source paper "camera obscurer generative art for design inspiration"):
  with members:
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "focal loss for dense object detection"
    - "draw deep networks for recognizing styles of artists who illustrate children s books"
    - "mask r cnn"
    - "sift meets cnn a decade survey of instance retrieval"
    - "scalable nonlinear embeddings for semantic category based image retrieval"
    - "visual search at pinterest"

Connected through hyperedge HE-5 (induced by source paper "the design and implementation of a real time visual search system on jd e commerce platform"):
  with members:
    - "visual search at ebay"
    - "visual search at pinterest"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "learning fine grained image similarity with deep ranking"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-6 (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"
    - "sampling matters in deep embedding learning"
    - "no fuss distance metric learning using proxies"
    - "visual discovery at pinterest"
    - "deep metric learning via facility location"

Connected through hyperedge HE-7 (induced by source paper "one shot item search with multimodal data"):
  with members:
    - "deepstyle multimodal search engine for fashion and interior design"
    - "enhancing the performance of convolutional neural networks on quality degraded datasets"
    - "visual search at ebay"
    - "visual discovery at pinterest"
    - "quality resilient deep neural networks"
    - "an empirical study on the effects of different types of noise in image classification tasks"
    - "enriching word vectors with subword information"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"

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)