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: "high performance offline handwritten chinese character recognition using googlenet and directional feature maps"
  Abstract: Just like its great success in solving many computer vision problems, the convolutional neural networks (CNN) provided new end-to-end approach to handwritten Chinese character recognition (HCCR) with very promising results in recent years. However, previous CNNs so far proposed for HCCR were neither deep enough nor slim enough. We show in this paper that, a deeper architecture can benefit HCCR a lot to achieve higher performance, meanwhile can be designed with less parameters. We also show that the traditional feature extraction methods, such as Gabor or gradient feature maps, are still useful for enhancing the performance of CNN. We design a streamlined version of GoogLeNet [13], which was original proposed for image classification in recent years with very deep architecture, for HCCR (denoted as HCCR-GoogLeNet). The HCCR-GoogLeNet we used is 19 layers deep but involves with only 7.26 million parameters. Experiments were conducted using the ICDAR 2013 offline HCCR competition dataset. It has been shown that with the proper incorporation with traditional directional feature maps, the proposed single and ensemble HCCR-GoogLeNet models achieve new state of the art recognition accuracy of 96.35% and 96.74%, respectively, outperforming previous best result with significant gap.

Connected through hyperedge HE-1 (induced by source paper "robust gabor networks"):
  with members:
    - "adversarial sensor attack on lidar based perception in autonomous driving"
    - "kervolutional neural networks"
    - "are self driving cars secure evasion attacks against deep neural networks for steering angle prediction"
    - "the singular values of convolutional layers"
    - "robustness via curvature regularization and vice versa"
    - "structured adversarial attack towards general implementation and better interpretability"
    - "gabor filter assisted energy efficient fast learning convolutional neural networks"
    - "deep watershed transform for instance segmentation"

Connected through hyperedge HE-2 (induced by source paper "learning complex basis functions for invariant representations of audio"):
  with members:
    - "gabor convolutional networks"
    - "learning transposition invariant interval features from symbolic music and audio"
    - "audio to score alignment using transposition invariant features"
    - "oriented response networks"
    - "deformable convolutional networks"
    - "end to end learning for music audio tagging at scale"
    - "transform invariant auto encoder"
    - "harmonic networks deep translation and rotation equivariance"

Connected through hyperedge HE-3 (induced by source paper "template instance loss for offline handwritten chinese character recognition"):
  with members:
    - "rethinking feature distribution for loss functions in image classification"
    - "arcface additive angular margin loss for deep face recognition"
    - "building efficient cnn architecture for offline handwritten chinese character recognition"
    - "additive margin softmax for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "building fast and compact convolutional neural networks for offline handwritten chinese character recognition"
    - "focal loss for dense object detection"
    - "online and offline handwritten chinese character recognition a comprehensive study and new benchmark"

Connected through hyperedge HE-4 (induced by source paper "handwriting recognition in low resource scripts using adversarial learning"):
  with members:
    - "confidence prediction for lexicon free ocr"
    - "vital visual tracking via adversarial learning"
    - "cross language framework for word recognition and spotting of indic scripts"
    - "synthetic data generation for indic handwritten text recognition"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "adversarial discriminative domain adaptation"
    - "attribute cnns for word spotting in handwritten documents"
    - "neural ctrl f segmentation free query by string word spotting in handwritten manuscript collections"

Connected through hyperedge HE-5 (induced by source paper "building efficient cnn architecture for offline handwritten chinese character recognition"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "building fast and compact convolutional neural networks for offline handwritten chinese character recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "channel pruning for accelerating very deep neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "online and offline handwritten chinese character recognition a comprehensive study and new benchmark"
    - "xnor net imagenet classification using binary convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "gabor convolutional networks"):
  with members:
    - "latent constrained correlation filter"
    - "one two one networks for compression artifacts reduction in remote sensing"
    - "gabor filter assisted energy efficient fast learning convolutional neural networks"
    - "oriented response networks"
    - "deformable convolutional networks"
    - "structured receptive fields in cnns"
    - "ti pooling transformation invariant pooling for feature learning in convolutional neural networks"
    - "wide residual networks"

Connected through hyperedge HE-7 (induced by source paper "trajectory based radical analysis network for online handwritten chinese character recognition"):
  with members:
    - "multi scale attention with dense encoder for handwritten mathematical expression recognition"
    - "a gru based encoder decoder approach with attention for online handwritten mathematical expression recognition"
    - "radical analysis network for zero shot learning in printed chinese character recognition"
    - "modeling coverage for neural machine translation"
    - "drawing and recognizing chinese characters with recurrent neural network"
    - "effective approaches to attention based neural machine translation"
    - "end to end attention based large vocabulary speech recognition"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-8 (induced by source paper "generating handwritten chinese characters using cyclegan"):
  with members:
    - "learning to write stylized chinese characters by reading a handful of examples"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "neural style transfer a review"
    - "online and offline handwritten chinese character recognition a comprehensive study and new benchmark"
    - "combining markov random fields and convolutional neural networks for image synthesis"
    - "fast patch based style transfer of arbitrary style"
    - "improved techniques for training gans"
    - "densely connected convolutional 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)