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: "document image classification with a specific view on applications of patent images"
  Abstract: The main focus of this chapter is document image classification and retrieval, where we analyse and compare different parameters for the run-length histogram and Fisher vector-based image representations. We do an exhaustive experimental study using different document image data sets, including the MARG benchmarks, two data sets built on customer data and the images from the patent image classification task of the CLEF-IP 2011. The aim of the study is to give guidelines on how to best choose the parameters such that the same features perform well on different tasks. As an example of such need, we describe the image-based patent retrieval tasks of CLEF-IP 2011, where we used the same image representation to predict the image type and retrieve relevant patents.

Connected through hyperedge HE-1 (induced by source paper "what is the right way to represent document images"):
  with members:
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "imagenet large scale visual recognition challenge"
    - "neural codes for image retrieval"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "visualizing and understanding convolutional networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)