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: "dress like a star retrieving fashion products from videos"
  Abstract: This work proposes a system for retrieving clothing and fashion products from video content. Although films and television are the perfect showcase for fashion brands to promote their products, spectators are not always aware of where to buy the latest trends they see on screen. Here, a framework for breaking the gap between fashion products shown on videos and users is presented. By relating clothing items and video frames in an indexed database and performing frame retrieval with temporal aggregation and fast indexing techniques, we can find fashion products from videos in a simple and non-intrusive way. Experiments in a large-scale dataset conducted here show that, by using the proposed framework, memory requirements can be reduced by 42.5X with respect to linear search, whereas accuracy is maintained at around 90%.

Connected through hyperedge HE-1 (induced by source paper "fashion retrieval via graph reasoning networks on a similarity pyramid"):
  with members:
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "netizen style commenting on fashion photos dataset and diversity measures"
    - "end to end deep kronecker product matching for person re identification"
    - "video2shop exactly matching clothes in videos to online shopping images"
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "deep randomized ensembles for metric learning"
    - "improving the annotation of deepfashion images for fine grained attribute recognition"
    - "person re identification with deep similarity guided graph neural network"

Connected through hyperedge HE-2 (induced by source paper "learning type aware embeddings for fashion compatibility"):
  with members:
    - "sampling matters in deep embedding learning"
    - "learning unified embedding for apparel recognition"
    - "self supervised learning of visual features through embedding images into text topic spaces"
    - "be your own prada fashion synthesis with structural coherence"
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"
    - "leveraging weakly annotated data for fashion image retrieval and label prediction"
    - "learning fashion compatibility with bidirectional lstms"
    - "mining fashion outfit composition using an end to end deep learning approach on set data"

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)