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: "image captioning with object detection and localization"
  Abstract: Automatically generating a natural language description of an image is a task close to the heart of image understanding. In this paper, we present a multi-model neural network method closely related to the human visual system that automatically learns to describe the content of images. Our model consists of two sub-models: an object detection and localization model, which extract the information of objects and their spatial relationship in images respectively; Besides, a deep recurrent neural network (RNN) based on long short-term memory (LSTM) units with attention mechanism for sentences generation. Each word of the description will be automatically aligned to different objects of the input image when it is generated. This is similar to the attention mechanism of the human visual system. Experimental results on the COCO dataset showcase the merit of the proposed method, which outperforms previous benchmark models.

Connected through hyperedge HE-1 (induced by source paper "real time steganalysis for stream media based on multi channel convolutional sliding windows"):
  with members:
    - "clinical assistant diagnosis for electronic medical record based on convolutional neural network"
    - "automatically generate steganographic text based on markov model and huffman coding"
    - "how to augment a small learning set for improving the performances of a cnn based steganalyzer"
    - "generating steganographic text with lstms"
    - "a novel convolutional neural network for image steganalysis with shared normalization"
    - "densely connected convolutional networks"
    - "fractalnet ultra deep neural networks without residuals"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-2 (induced by source paper "ts rnn text steganalysis based on recurrent neural networks"):
  with members:
    - "real time steganalysis for stream media based on multi channel convolutional sliding windows"
    - "ts cnn text steganalysis from semantic space based on convolutional neural network"
    - "clinical assistant diagnosis for electronic medical record based on convolutional neural network"
    - "automatically generate steganographic text based on markov model and huffman coding"
    - "generating steganographic text with lstms"
    - "neural responding machine for short text conversation"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

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)