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: "query by string word spotting based on character bi gram indexing"
  Abstract: In this paper we propose a segmentation-free query by string word spotting method. Both the documents and query strings are encoded using a recently proposed word representation that projects images and strings into a common attribute space based on a Pyramidal Histogram of Characters (PHOC). These attribute models are learned using linear SVMs over the Fisher Vector [8] representation of the images along with the PHOC labels of the corresponding strings. In order to search through the whole page, document regions are indexed per character bi-gram using a similar attribute representation. On top of that, we propose an integral image representation of the document using a simplified version of the attribute model for efficient computation. Finally we introduce a re-ranking step in order to boost retrieval performance. We show state-of-the-art results for segmentation-free query by string word spotting in single-writer and multi-writer standard datasets.

Connected through hyperedge HE-1 (induced by source paper "a multi oriented chinese keyword spotter guided by text line detection"):
  with members:
    - "textboxes a single shot oriented scene text detector"
    - "mask textspotter an end to end trainable neural network for spotting text with arbitrary shapes"
    - "towards end to end text spotting with convolutional recurrent neural networks"
    - "neural ctrl f segmentation free query by string word spotting in handwritten manuscript collections"
    - "synthetic data for text localisation in natural images"
    - "textboxes a fast text detector with a single deep neural network"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "reading text in the wild with convolutional neural networks"

Connected through hyperedge HE-2 (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"
    - "matching handwritten document images"

Connected through hyperedge HE-3 (induced by source paper "single shot scene text retrieval"):
  with members:
    - "an end to end textspotter with explicit alignment and attention"
    - "textboxes a single shot oriented scene text detector"
    - "towards end to end text spotting with convolutional recurrent neural networks"
    - "integrating scene text and visual appearance for fine grained image classification"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"

Connected through hyperedge HE-4 (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"
    - "matching handwritten document images"
    - "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"

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)