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: "deepfont identify your font from an image"
  Abstract: As font is one of the core design concepts, automatic font identification and similar font suggestion from an image or photo has been on the wish list of many designers. We study the Visual Font Recognition (VFR) problem, and advance the state-of-the-art remarkably by developing the DeepFont system. First of all, we build up the first available large-scale VFR dataset, named AdobeVFR, consisting of both labeled synthetic data and partially labeled real-world data. Next, to combat the domain mismatch between available training and testing data, we introduce a Convolutional Neural Network (CNN) decomposition approach, using a domain adaptation technique based on a Stacked Convolutional Auto-Encoder (SCAE) that exploits a large corpus of unlabeled real-world text images combined with synthetic data preprocessed in a specific way. Moreover, we study a novel learning-based model compression approach, in order to reduce the DeepFont model size without sacrificing its performance. The DeepFont system achieves an accuracy of higher than 80% (top-5) on our collected dataset, and also produces a good font similarity measure for font selection and suggestion. We also achieve around 6 times compression of the model without any visible loss of recognition accuracy.

Connected through hyperedge HE-1 (induced by source paper "character independent font identification"):
  with members:
    - "automatic generation of typographic font from small font subset"
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "glyphgan style consistent font generation based on generative adversarial networks"
    - "serif or sans visual font analytics on book covers and online advertisements"
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "exploring disentangled feature representation beyond face identification"
    - "learning features for offline handwritten signature verification using deep convolutional neural networks"
    - "font identification in historical documents using active learning"

Connected through hyperedge HE-2 (induced by source paper "controllable artistic text style transfer via shape matching gan"):
  with members:
    - "context aware text based binary image stylization and synthesis"
    - "adjustable real time style transfer"
    - "a style aware content loss for real time hd style transfer"
    - "tet gan text effects transfer via stylization and destylization"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "diversified texture synthesis with feed forward networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "multi content gan for few shot font style transfer"

Connected through hyperedge HE-3 (induced by source paper "large scale tag based font retrieval with generative feature learning"):
  with members:
    - "stacked cross attention for image text matching"
    - "identity aware textual visual matching with latent co attention"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "person search with natural language description"
    - "dual path convolutional image text embedding with instance loss"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "learning to compare relation network for few shot learning"
    - "multi content gan for few shot font style transfer"

Connected through hyperedge HE-4 (induced by source paper "transform invariant convolutional neural networks for image classification and search"):
  with members:
    - "ti pooling transformation invariant pooling for feature learning in convolutional neural networks"
    - "interactive inter layer activeness propagation"
    - "learning attributes equals multi source domain generalization"
    - "jointly modeling embedding and translation to bridge video and language"
    - "contextual action recognition with r cnn"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-5 (induced by source paper "learning to infer user interface attributes from images"):
  with members:
    - "3d interpreter networks for viewer centered wireframe modeling"
    - "image generation from scene graphs"
    - "mask textspotter an end to end trainable neural network for spotting text with arbitrary shapes"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "pix2code generating code from a graphical user interface screenshot"
    - "learning to infer graphics programs from hand drawn images"
    - "learning from simulated and unsupervised images through adversarial training"
    - "synthetic data for text localisation in natural images"

Connected through hyperedge HE-6 (induced by source paper "real world underwater enhancement challenging benchmark and efficient solutions"):
  with members:
    - "does haze removal help cnn based image classification"
    - "watergan unsupervised generative network to enable real time color correction of monocular underwater images"
    - "yolov3 an incremental improvement"
    - "densely connected pyramid dehazing network"
    - "towards quality advancement of underwater machine vision with generative adversarial networks"
    - "learning aggregated transmission propagation networks for haze removal and beyond"
    - "dehazenet an end to end system for single image haze removal"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "enhance visual recognition under adverse conditions via deep networks"):
  with members:
    - "how image degradations affect deep cnn based face recognition"
    - "understanding how image quality affects deep neural networks"
    - "studying very low resolution recognition using deep networks"
    - "learning sparse feature representations using probabilistic quadtrees and deep belief nets"
    - "wider face a face detection benchmark"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "fast and robust multiple colorchecker detection using deep convolutional neural networks"):
  with members:
    - "using synthetic data to train neural networks is model based reasoning"
    - "synthetic data for text localisation in natural images"
    - "going deeper with convolutions"
    - "synthetic data and artificial neural networks for natural scene text recognition"
    - "reading text in the wild with convolutional neural networks"
    - "multi digit number recognition from street view imagery using deep convolutional neural 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)