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: "building fast and compact convolutional neural networks for offline handwritten chinese character recognition"
  Abstract: Like other problems in computer vision, offline handwritten Chinese character recognition (HCCR) has achieved impressive results using convolutional neural network (CNN)-based methods. However, larger and deeper networks are needed to deliver state-of-the-art results in this domain. Such networks intuitively appear to incur high computational cost, and require the storage of a large number of parameters, which renders them unfeasible for deployment in portable devices. To solve this problem, we propose a Global Supervised Low-rank Expansion (GSLRE) method and an Adaptive Drop-weight (ADW) technique to solve the problems of speed and storage capacity. We design a nine-layer CNN for HCCR consisting of 3,755 classes, and devise an algorithm that can reduce the networks computational cost by nine times and compress the network to 1/18 of the original size of the baseline model, with only a 0.21% drop in accuracy. In tests, the proposed algorithm surpassed the best single-network performance reported thus far in the literature while requiring only 2.3 MB for storage. Furthermore, when integrated with our effective forward implementation, the recognition of an offline character image took only 9.7 ms on a CPU. Compared with the state-of-the-art CNN model for HCCR, our approach is approximately 30 times faster, yet 10 times more cost efficient.

Connected through hyperedge HE-1 (induced by source paper "pioneer dataset and automatic recognition of urdu handwritten characters using a deep autoencoder and convolutional neural network"):
  with members:
    - "building efficient cnn architecture for offline handwritten chinese character recognition"
    - "generative adversarial network for medical images mi gan"
    - "improved deep convolutional neural network for online handwritten chinese character recognition using domain specific knowledge"
    - "convolutional neural networks applied to house numbers digit classification"

Connected through hyperedge HE-2 (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-3 (induced by source paper "pruning by explaining a novel criterion for deep neural network pruning"):
  with members:
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "importance estimation for neural network pruning"
    - "compact and computationally efficient representation of deep neural networks"
    - "methods for interpreting and understanding deep neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "nisp pruning networks using neuron importance score propagation"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "structured probabilistic pruning for convolutional neural network acceleration"

Connected through hyperedge HE-4 (induced by source paper "network implosion effective model compression for resnets via static layer pruning and retraining"):
  with members:
    - "learning strict identity mappings in deep residual networks"
    - "data driven sparse structure selection for deep neural networks"
    - "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes"
    - "convolutional sequence to sequence learning"
    - "adaptive learning rate via covariance matrix based preconditioning for deep neural networks"
    - "learning structured sparsity in deep neural networks"
    - "identity mappings in deep residual networks"
    - "language modeling with gated convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "fully convolutional intensive feature flow neural network for text recognition"):
  with members:
    - "bubblenets learning to select the guidance frame in video object segmentation by deep sorting frames"
    - "scene text recognition from two dimensional perspective"
    - "show attend and read a simple and strong baseline for irregular text recognition"
    - "deep matching prior network toward tighter multi oriented text detection"
    - "stn ocr a single neural network for text detection and text recognition"
    - "aon towards arbitrarily oriented text recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "reading scene text with attention convolutional sequence modeling"

Connected through hyperedge HE-6 (induced by source paper "fast densenet towards efficient and accurate text recognition with fast dense networks"):
  with members:
    - "bubblenets learning to select the guidance frame in video object segmentation by deep sorting frames"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "scene text recognition from two dimensional perspective"
    - "densereg fully convolutional dense shape regression in the wild"
    - "residual dense network for image super resolution"
    - "aon towards arbitrarily oriented text recognition"
    - "reading scene text with attention convolutional sequence modeling"
    - "deep forest towards an alternative to deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "handwritten chinese font generation with collaborative stroke refinement"):
  with members:
    - "learning discriminative video representations using adversarial perturbations"
    - "a style aware content loss for real time hd style transfer"
    - "auto encoder guided gan for chinese calligraphy synthesis"
    - "learning to write stylized chinese characters by reading a handful of examples"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "unsupervised diverse colorization via generative adversarial networks"
    - "multi content gan for few shot font style transfer"
    - "separating style and content for generalized style transfer"

Connected through hyperedge HE-8 (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"
    - "focal loss for dense object detection"
    - "online and offline handwritten chinese character recognition a comprehensive study and new benchmark"
    - "facenet a unified embedding for face recognition and clustering"

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)