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: "deep adaptive learning for writer identification based on single handwritten word images"
  Abstract: Abstract   There are two types of information in each handwritten word image: explicit information which can be easily read or derived directly, such as lexical content or word length, and implicit attributes such as the author’s identity. Whether features learned by a neural network for one task can be used for another task remains an open question. In this paper, we present a deep adaptive learning method for writer identification based on single-word images using multi-task learning. An auxiliary task is added to the training process to enforce the emergence of reusable features. Our proposed method transfers the benefits of the learned features of a convolutional neural network from an auxiliary task such as explicit content recognition to the main task of writer identification in a single procedure. Specifically, we propose a new adaptive convolutional layer to exploit the learned deep features. A multi-task neural network with one or several adaptive convolutional layers is trained end-to-end, to exploit robust generic features for a specific main task, i.e., writer identification. Three auxiliary tasks, corresponding to three explicit attributes of handwritten word images (lexical content, word length and character attributes), are evaluated. Experimental results on two benchmark datasets show that the proposed deep adaptive learning method can improve the performance of writer identification based on single-word images, compared to non-adaptive and simple linear-adaptive approaches.

Connected through hyperedge HE-1 (induced by source paper "fragnet writer identification using deep fragment networks"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "semi supervised feature learning for improving writer identification"
    - "learn to pay attention"
    - "unsupervised feature learning for writer identification and writer retrieval"
    - "deepwriter a multi stream deep cnn for text independent writer identification"
    - "feature pyramid networks for object detection"
    - "deep residual learning for image recognition"
    - "fast r cnn"

Connected through hyperedge HE-2 (induced by source paper "deep generalized max pooling"):
  with members:
    - "semi supervised feature learning for improving writer identification"
    - "on the convergence of adam and beyond"
    - "precision learning towards use of known operators in neural networks"
    - "in defense of the triplet loss for person re identification"
    - "unsupervised feature learning for writer identification and writer retrieval"
    - "encoding cnn activations for writer recognition"
    - "convolutional neural networks for font classification"
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"

Connected through hyperedge HE-3 (induced by source paper "offline writer identification based on the path signature feature"):
  with members:
    - "toward high performance online hccr"
    - "developing the path signature methodology and its application to landmark based human action recognition"
    - "leveraging the path signature for skeleton based human action recognition"
    - "learning spatial semantic context with fully convolutional recurrent network for online handwritten chinese text recognition"
    - "sparse radial sampling lbp for writer identification"
    - "deepwriterid an end to end online text independent writer identification system"
    - "rotation invariants of two dimensional curves based on iterated integrals"

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)