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: "recognition confidence analysis of handwritten chinese character with cnn"
  Abstract: In this paper, we present an effective method to analyze the recognition confidence of handwritten Chinese character, based on the softmax regression score of a high performance convolutional neural networks (CNN). Through careful and thorough statistics of 827,685 testing samples that randomly selected from total 8836 different classes of Chinese characters, we find that the confidence measurement based on CNN is an useful metric to know how reliable the recognition results are. Furthermore, we find by experiments that the recognition confidence can be used to find out similar and confusable character-pairs, to check wrongly or cursively written samples, and even to discover and correct mis-labelled samples. Many interesting observations and statistics are given and analyzed in this study.

Connected through hyperedge HE-1 (induced by source paper "online and offline handwritten chinese character recognition a comprehensive study and new benchmark"):
  with members:
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "character level chinese writer identification using path signature feature dropstroke and deep cnn"
    - "learning transferable features with deep adaptation networks"
    - "reading scene text in deep convolutional sequences"
    - "dropsample a new training method to enhance deep convolutional neural networks for large scale unconstrained handwritten chinese character recognition"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "high performance offline handwritten chinese character recognition using googlenet and directional feature maps"

Connected through hyperedge HE-2 (induced by source paper "dropsample a new training method to enhance deep convolutional neural networks for large scale unconstrained handwritten chinese character recognition"):
  with members:
    - "improved deep convolutional neural network for online handwritten chinese character recognition using domain specific knowledge"
    - "going deeper with convolutions"
    - "deep learning face representation by joint identification verification"
    - "synthetic data and artificial neural networks for natural scene text recognition"
    - "deeppose human pose estimation via deep neural networks"
    - "speech recognition with deep recurrent neural networks"
    - "network in network"
    - "sparse arrays of signatures for online character recognition"

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)