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: "multi column deep neural networks for offline handwritten chinese character classification"
  Abstract: Our Multi-Column Deep Neural Networks achieve best known recognition rates on Chinese characters from the ICDAR 2011 and 2013 offline handwriting competitions, approaching human performance.

Connected through hyperedge HE-1 (induced by source paper "fingertip detection and tracking for recognition of air writing in videos"):
  with members:
    - "analysis of hand segmentation in the wild"
    - "learning to estimate 3d hand pose from single rgb images"
    - "using a single rgb frame for real time 3d hand pose estimation in the wild"
    - "toward high performance online hccr"
    - "emnist an extension of mnist to handwritten letters"
    - "design of a very compact cnn classifier for online handwritten chinese character recognition using dropweight and global pooling"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "recognizing fine grained and composite activities using hand centric features and script data"

Connected through hyperedge HE-2 (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-3 (induced by source paper "denseran for offline handwritten chinese character recognition"):
  with members:
    - "multi scale attention with dense encoder for handwritten mathematical expression recognition"
    - "radical analysis network for zero shot learning in printed chinese character recognition"
    - "densely connected convolutional networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "high performance offline handwritten chinese character recognition using googlenet and directional feature maps"
    - "natural language understanding with distributed representation"
    - "very deep convolutional networks for large scale image recognition"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-4 (induced by source paper "building fast and compact convolutional neural networks for offline handwritten chinese character recognition"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "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"
    - "learning spatial semantic context with fully convolutional recurrent network for online handwritten chinese text recognition"
    - "dynamic network surgery for efficient dnns"
    - "drawing and recognizing chinese characters with recurrent neural network"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-5 (induced by source paper "deep multi scale location aware 3d convolutional neural networks for automated detection of lacunes of presumed vascular origin"):
  with members:
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "location sensitive deep convolutional neural networks for segmentation of white matter hyperintensities"
    - "automatic segmentation of mr brain images with a convolutional neural network"
    - "deep learning in neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "apac augmented pattern classification with neural networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "online and offline handwritten chinese character recognition a comprehensive study and new benchmark"):
  with members:
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "character level chinese writer identification using path signature feature dropstroke and deep cnn"
    - "learning transferable features with deep adaptation networks"
    - "reading scene text in deep convolutional sequences"
    - "dropsample a new training method to enhance deep convolutional neural networks for large scale unconstrained handwritten chinese character recognition"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "high performance offline handwritten chinese character recognition using googlenet and directional feature maps"

Connected through hyperedge HE-7 (induced by source paper "location sensitive deep convolutional neural networks for segmentation of white matter hyperintensities"):
  with members:
    - "deep multi scale location aware 3d convolutional neural networks for automated detection of lacunes of presumed vascular origin"
    - "brain tumor segmentation with deep neural networks"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "voxresnet deep voxelwise residual networks for volumetric brain segmentation"
    - "hemis hetero modal image segmentation"
    - "hough cnn deep learning for segmentation of deep brain regions in mri and ultrasound"
    - "automatic segmentation of mr brain images with a convolutional neural network"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-8 (induced by source paper "deep learning in neural networks"):
  with members:
    - "going deeper with convolutions"
    - "event driven contrastive divergence for spiking neuromorphic systems"
    - "a clockwork rnn"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "sequence to sequence learning with neural networks"
    - "dropout improves recurrent neural networks for handwriting recognition"
    - "efficient visual coding from retina to v2"
    - "decaf a deep convolutional activation feature for generic visual recognition"

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)