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: "phocnet a deep convolutional neural network for word spotting in handwritten documents"
  Abstract: In recent years, deep convolutional neural networks have achieved state of the art performance in various computer vision task such as classification, detection or segmentation. Due to their outstanding performance, CNNs are more and more used in the field of document image analysis as well. In this work, we present a CNN architecture that is trained with the recently proposed PHOC representation. We show empirically that our CNN architecture is able to outperform state of the art results for various word spotting benchmarks while exhibiting short training and test times.

Connected through hyperedge HE-1 (induced by source paper "annotation free learning of deep representations for word spotting using synthetic data and self labeling"):
  with members:
    - "exploring confidence measures for word spotting in heterogeneous datasets"
    - "hwnet v2 an efficient word image representation for handwritten documents"
    - "attribute cnns for word spotting in handwritten documents"
    - "learning deep representations for word spotting under weak supervision"

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"
    - "matching handwritten document images"
    - "scan attend and read end to end handwritten paragraph recognition with mdlstm attention"

Connected through hyperedge HE-3 (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"
    - "unsupervised feature learning for writer identification and writer retrieval"
    - "encoding cnn activations for writer recognition"
    - "convolutional neural networks for font classification"

Connected through hyperedge HE-4 (induced by source paper "a two stage method for text line detection in historical documents"):
  with members:
    - "baseline detection in historical documents using convolutional u nets"
    - "dhsegment a generic deep learning approach for document segmentation"
    - "multi task handwritten document layout analysis"
    - "pagenet page boundary extraction in historical handwritten documents"
    - "read bad a new dataset and evaluation scheme for baseline detection in archival documents"
    - "full page text recognition learning where to start and when to stop"
    - "regular expressions for decoding of neural network outputs"
    - "joint line segmentation and transcription for end to end handwritten paragraph recognition"

Connected through hyperedge HE-5 (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"
    - "deepwriter a multi stream deep cnn for text independent writer identification"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "fast r cnn"

Connected through hyperedge HE-6 (induced by source paper "a large scale field test on word image classification in large historical document collections using a traditional and two deep learning methods"):
  with members:
    - "no padding please efficient neural handwriting recognition"
    - "open set chinese character recognition using multi typed attributes"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "fast search with poor ocr"):
  with members:
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "word translation without parallel data"
    - "correlational neural networks"
    - "approximating edit distance in near linear time"
    - "fast k nearest neighbor search using gpu"

Connected through hyperedge HE-8 (induced by source paper "pattern spotting in historical documents using convolutional models"):
  with members:
    - "discovering visual patterns in art collections with spatially consistent feature learning"
    - "focal loss for dense object detection"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "feature pyramid networks for object detection"
    - "microsoft coco common objects in context"

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)