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: "enhanced image classification with a fast learning shallow convolutional neural network"
  Abstract: We present a neural network architecture and training method designed to enable very rapid training and low implementation complexity. Due to its training speed and very few tunable parameters, the method has strong potential for applications requiring frequent retraining or online training. The approach is characterized by (a) convolutional filters based on biologically inspired visual processing filters, (b) randomly-valued classifier-stage input weights, (c) use of least squares regression to train the classifier output weights in a single batch, and (d) linear classifier-stage output units. We demonstrate the efficacy of the method by applying it to image classification. Our results match existing state-of-the-art results on the MNIST (0.37% error) and NORB-small (2.2% error) image classification databases, but with very fast training times compared to standard deep network approaches. The network's performance on the Google Street View House Number (SVHN) (4% error) database is also competitive with state-of-the art methods.

Connected through hyperedge HE-1 (induced by source paper "unsupervised learning using pretrained cnn and associative memory bank"):
  with members:
    - "dense associative memory is robust to adversarial inputs"
    - "dynamic routing between capsules"
    - "scatternet hybrid deep learning shdl network for object classification"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "dense associative memory for pattern recognition"
    - "a theoretical analysis of deep neural networks for texture classification"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "learning multi domain convolutional neural networks for visual tracking"

Connected through hyperedge HE-2 (induced by source paper "convolution with logarithmic filter groups for efficient shallow cnn"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep networks with stochastic depth"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "fractalnet ultra deep neural networks without residuals"
    - "deep roots improving cnn efficiency with hierarchical filter groups"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "training very deep networks"

Connected through hyperedge HE-3 (induced by source paper "track everything limiting prior knowledge in online multi object recognition"):
  with members:
    - "mot16 a benchmark for multi object tracking"
    - "understanding data augmentation for classification when to warp"
    - "deep learning in neural networks"
    - "fast simple and accurate handwritten digit classification by training shallow neural network classifiers with the extreme learning machine algorithm"
    - "motchallenge 2015 towards a benchmark for multi target tracking"
    - "very deep convolutional networks for large scale image recognition"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "overfeat integrated recognition localization and detection using convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "cifar 10 knn based ensemble of classifiers"):
  with members:
    - "wide residual networks"
    - "all you need is a good init"
    - "batch normalized maxout network in network"
    - "renet a recurrent neural network based alternative to convolutional networks"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "empirical evaluation of rectified activations in convolutional network"
    - "pcanet a simple deep learning baseline for image classification"
    - "ensemble classifiers and their applications a review"

Connected through hyperedge HE-5 (induced by source paper "understanding data augmentation for classification when to warp"):
  with members:
    - "deep residual learning for image recognition"
    - "learning classifiers from synthetic data using a multichannel autoencoder"
    - "apac augmented pattern classification with neural networks"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "some improvements on deep convolutional neural network based image classification"
    - "deep big simple neural nets for handwritten digit recognition"

Connected through hyperedge HE-6 (induced by source paper "an exploration of parameter redundancy in deep networks with circulant projections"):
  with members:
    - "fast simple and accurate handwritten digit classification by training shallow neural network classifiers with the extreme learning machine algorithm"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "exploiting linear structure within convolutional networks for efficient evaluation"
    - "fitnets hints for thin deep nets"
    - "going deeper with convolutions"
    - "deep fried convnets"
    - "speeding up convolutional neural networks with low rank expansions"
    - "caffe convolutional architecture for fast feature embedding"

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.NE(Neural and Evolutionary Computing)