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: "online and offline handwritten chinese character recognition a comprehensive study and new benchmark"
  Abstract: Recent deep learning based methods have achieved the state-of-the-art performance for handwritten Chinese character recognition (HCCR) by learning discriminative representations directly from raw data. Nevertheless, we believe that the long-and-well investigated domain-specific knowledge should still help to boost the performance of HCCR. By integrating the traditional normalization-cooperated direction-decomposed feature map (directMap) with the deep convolutional neural network (convNet), we are able to obtain new highest accuracies for both online and offline HCCR on the ICDAR-2013 competition database. With this new framework, we can eliminate the needs for data augmentation and model ensemble, which are widely used in other systems to achieve their best results. This makes our framework to be efficient and effective for both training and testing. Furthermore, although directMap+convNet can achieve the best results and surpass human-level performance, we show that writer adaptation in this case is still effective. A new adaptation layer is proposed to reduce the mismatch between training and test data on a particular source layer. The adaptation process can be efficiently and effectively implemented in an unsupervised manner. By adding the adaptation layer into the pre-trained convNet, it can adapt to the new handwriting styles of particular writers, and the recognition accuracy can be further improved consistently and significantly. This paper gives an overview and comparison of recent deep learning based approaches for HCCR, and also sets new benchmarks for both online and offline HCCR.

Connected through hyperedge HE-1 (induced by source paper "joint architecture and knowledge distillation in convolutional neural network for offline handwritten chinese text recognition"):
  with members:
    - "knowledge adaptation for efficient semantic segmentation"
    - "flightnns lightweight quantized deep neural networks for fast and accurate inference"
    - "structured knowledge distillation for dense prediction"
    - "hetconv heterogeneous kernel based convolutions for deep cnns"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "writer aware cnn for parsimonious hmm based offline handwritten chinese text recognition"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-2 (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"
    - "cross stitch networks for multi task learning"
    - "deepwriter a multi stream deep cnn for text independent writer identification"
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "fast r cnn"

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"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-4 (induced by source paper "end to end recognition system for recognizing offline unconstrained vietnamese handwriting"):
  with members:
    - "multi scale attention with dense encoder for handwritten mathematical expression recognition"
    - "densely connected convolutional networks"
    - "image to markup generation with coarse to fine attention"

Connected through hyperedge HE-5 (induced by source paper "handwritten amharic character recognition using a convolutional neural network"):
  with members:
    - "building fast and compact convolutional neural networks for offline handwritten chinese character recognition"
    - "handwritten isolated bangla compound character recognition a new benchmark using a novel deep learning approach"

Connected through hyperedge HE-6 (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"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-7 (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"
    - "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"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "robust classification with convolutional prototype learning"):
  with members:
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep direct regression for multi oriented scene text detection"
    - "mask r cnn"
    - "r fcn object detection via region based fully convolutional networks"
    - "learning to segment object candidates"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "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)