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: "deepstyle multimodal search engine for fashion and interior design"
  Abstract: In this paper, we propose a multimodal search engine that combines visual and textual cues to retrieve items from a multimedia database aesthetically similar to the query. The goal of our engine is to enable intuitive retrieval of fashion merchandise such as clothes or furniture. Existing search engines treat textual input only as an additional source of information about the query image and do not correspond to the real-life scenario where the user looks for 'the same shirt but of denim'. Our novel method, dubbed DeepStyle, mitigates those shortcomings by using a joint neural network architecture to model contextual dependencies between features of different modalities. We prove the robustness of this approach on two different challenging datasets of fashion items and furniture where our DeepStyle engine outperforms baseline methods by 18-21% on the tested datasets. Our search engine is commercially deployed and available through a Web-based application.

Connected through hyperedge HE-1 (induced by source paper "pog personalized outfit generation for fashion recommendation at alibaba ifashion"):
  with members:
    - "bert4rec sequential recommendation with bidirectional encoder representations from transformer"
    - "learning type aware embeddings for fashion compatibility"
    - "deep contextualized word representations"
    - "outfit generation and style extraction via bidirectional lstm and autoencoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "self attentive sequential recommendation"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "neural compatibility modeling with attentive knowledge distillation"

Connected through hyperedge HE-2 (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"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "deep learning based large scale visual recommendation and search for e commerce"
    - "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-3 (induced by source paper "one shot item search with multimodal data"):
  with members:
    - "web scale responsive visual search at bing"
    - "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)