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: "matching handwritten document images"
  Abstract: We address the problem of predicting similarity between a pair of handwritten document images written by different individuals. This has applications related to matching and mining in image collections containing handwritten content. A similarity score is computed by detecting patterns of text re-usages between document images irrespective of the minor variations in word morphology, word ordering, layout and paraphrasing of the content. Our method does not depend on an accurate segmentation of words and lines. We formulate the document matching problem as a structured comparison of the word distributions across two document images. To match two word images, we propose a convolutional neural network (CNN) based feature descriptor. Performance of this representation surpasses the state-of-the-art on handwritten word spotting. Finally, we demonstrate the applicability of our method on a practical problem of matching handwritten assignments.

Connected through hyperedge HE-1 (induced by source paper "neural word search in historical manuscript collections"):
  with members:
    - "textboxes a single shot oriented scene text detector"
    - "towards end to end text spotting with convolutional recurrent neural networks"
    - "attribute cnns for word spotting in handwritten documents"
    - "neural ctrl f segmentation free query by string word spotting in handwritten manuscript collections"
    - "information theoretical label embeddings for large scale image classification"
    - "identity mappings in deep residual networks"
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"
    - "scan attend and read end to end handwritten paragraph recognition with mdlstm attention"

Connected through hyperedge HE-2 (induced by source paper "exploring confidence measures for word spotting in heterogeneous datasets"):
  with members:
    - "principled uncertainty estimation for deep neural networks"
    - "attribute cnns for word spotting in handwritten documents"
    - "learning deep representations for word spotting under weak supervision"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"

Connected through hyperedge HE-3 (induced by source paper "hwnet v2 an efficient word image representation for handwritten documents"):
  with members:
    - "neural word search in historical manuscript collections"
    - "attribute cnns for word spotting in handwritten documents"
    - "neural ctrl f segmentation free query by string word spotting in handwritten manuscript collections"
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"
    - "understanding neural networks through deep visualization"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"

Connected through hyperedge HE-4 (induced by source paper "attribute cnns for word spotting in handwritten documents"):
  with members:
    - "learning deep representations for word spotting under weak supervision"
    - "information theoretical label embeddings for large scale image classification"
    - "pn net conjoined triple deep network for learning local image descriptors"
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"
    - "densecap fully convolutional localization networks for dense captioning"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "neural ctrl f segmentation free query by string word spotting in handwritten manuscript collections"):
  with members:
    - "identity mappings in deep residual networks"
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"
    - "query by string word spotting based on character bi gram indexing"
    - "spatial transformer networks"
    - "densecap fully convolutional localization networks for dense captioning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-6 (induced by source paper "learning deep representations for word spotting under weak supervision"):
  with members:
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "very deep convolutional networks for large scale image recognition"
    - "spatial pyramid pooling in deep convolutional networks for visual 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)