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: "the resale price prediction of secondhand jewelry items using a multi modal deep model with iterative co attention"
  Abstract: The resale price assessment of secondhand jewelry items relies heavily on the individual knowledge and skill of domain experts. In this paper, we propose a methodology for reconstructing an AI system that autonomously assesses the resale prices of secondhand jewelry items without the need for professional knowledge. As shown in recent studies on fashion items, multimodal approaches combining specifications and visual information of items have succeeded in obtaining fine-grained representations of fashion items, although they generally apply simple vector operations through a multimodal fusion. We similarly build a multimodal model using images and attributes of the product and further employ state-of-the-art multimodal deep neural networks applied in computer vision to achieve a practical performance level. In addition, we model the pricing procedure of an expert using iterative co-attention networks in which the appearance and attributes of the product are carefully and iteratively observed. Herein, we demonstrate the effectiveness of our model using a large dataset of secondhand no brand jewelry items received from a collaborating fashion retailer, and show that the iterative co-attention process operates effectively in the context of resale price prediction. Our model architecture is widely applicable to other fashion items where appearance and specifications are important aspects.

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)