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: "deepwriter a multi stream deep cnn for text independent writer identification"
  Abstract: Text-independent writer identification is challenging due to the huge variation of written contents and the ambiguous written styles of different writers. This paper proposes DeepWriter, a deep multi-stream CNN to learn deep powerful representation for recognizing writers. DeepWriter takes local handwritten patches as input and is trained with softmax classification loss. The main contributions are: 1) we design and optimize multi-stream structure for writer identification task; 2) we introduce data augmentation learning to enhance the performance of DeepWriter; 3) we introduce a patch scanning strategy to handle text image with different lengths. In addition, we find that different languages such as English and Chinese may share common features for writer identification, and joint training can yield better performance. Experimental results on IAM and HWDB datasets show that our models achieve high identification accuracy: 99.01% on 301 writers and 97.03% on 657 writers with one English sentence input, 93.85% on 300 writers with one Chinese character input, which outperform previous methods with a large margin. Moreover, our models obtain accuracy of 98.01% on 301 writers with only 4 English alphabets as input.

Connected through hyperedge HE-1 (induced by source paper "fragnet writer identification using deep fragment networks"):
  with members:
    - "deep adaptive learning for writer identification based on single handwritten word images"
    - "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"
    - "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 adaptive learning for writer identification based on single handwritten word images"):
  with members:
    - "an overview of multi task learning in deep neural networks"
    - "self transfer learning for fully weakly supervised object localization"
    - "online and offline handwritten chinese character recognition a comprehensive study and new benchmark"
    - "cross stitch networks for multi task learning"
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "fast r cnn"

Connected through hyperedge HE-3 (induced by source paper "semi supervised feature learning for improving writer identification"):
  with members:
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "unsupervised feature learning for writer identification and writer retrieval"
    - "a siamese long short term memory architecture for human re identification"
    - "convolutional patch representations for image retrieval an unsupervised approach"
    - "deep residual learning for image recognition"
    - "semi supervised learning with ladder networks"
    - "rethinking the inception architecture for computer vision"
    - "sparse radial sampling lbp for writer identification"

Connected through hyperedge HE-4 (induced by source paper "an empirical study on writer identification verification from intra variable individual handwriting"):
  with members:
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "xception deep learning with depthwise separable convolutions"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "rethinking the inception architecture for computer vision"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "encoding cnn activations for writer recognition"):
  with members:
    - "selective deep convolutional features for image retrieval"
    - "identity mappings in deep residual networks"
    - "convolutional patch representations for image retrieval an unsupervised approach"
    - "interferences in match kernels"
    - "deep residual learning for image recognition"
    - "exploiting local features from deep networks for image retrieval"
    - "generalized max pooling"
    - "multi scale orderless pooling of deep convolutional activation features"

Connected through hyperedge HE-6 (induced by source paper "unsupervised feature learning for writer identification and writer retrieval"):
  with members:
    - "identity mappings in deep residual networks"
    - "convolutional patch representations for image retrieval an unsupervised approach"
    - "deep residual learning for image recognition"
    - "sparse radial sampling lbp for writer identification"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "discriminative unsupervised feature learning with exemplar convolutional neural networks"
    - "cnn features off the shelf an astounding baseline for 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)