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: "unsupervised feature learning for writer identification and writer retrieval"
  Abstract: Deep Convolutional Neural Networks (CNN) have shown great success in supervised classification tasks such as character classification or dating. Deep learning methods typically need a lot of annotated training data, which is not available in many scenarios. In these cases, traditional methods are often better than or equivalent to deep learning methods. In this paper, we propose a simple, yet effective, way to learn CNN activation features in an unsupervised manner. Therefore, we train a deep residual network using surrogate classes. The surrogate classes are created by clustering the training dataset, where each cluster index represents one surrogate class. The activations from the penultimate CNN layer serve as features for subsequent classification tasks. We evaluate the feature representations on two publicly available datasets. The focus lies on the ICDAR17 competition dataset on historical document writer identification (Historical-WI). We show that the activation features we trained without supervision are superior to descriptors of state-of-the-art writer identification methods. Additionally, we achieve comparable results in the case of handwriting classification using the ICFHR16 competition dataset on historical Latin script types (CLaMM16).

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"
    - "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:
    - "deep adaptive learning for writer identification based on single handwritten word images"
    - "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"
    - "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 "automatic classification of defective photovoltaic module cells in electroluminescence images"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "segmentation of photovoltaic module cells in electroluminescence images"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "convolutional patch representations for image retrieval an unsupervised approach"
    - "exploiting local features from deep networks for image retrieval"
    - "learning deep features for discriminative localization"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (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"
    - "deepwriter a multi stream deep cnn for text independent writer identification"
    - "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"

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)