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: "fast simple and accurate handwritten digit classification by training shallow neural network classifiers with the extreme learning machine algorithm"
  Abstract: Recent advances in training deep (multi-layer) architectures have inspired a renaissance in neural network use. For example, deep convolutional networks are becoming the default option for difficult tasks on large datasets, such as image and speech recognition. However, here we show that error rates below 1% on the MNIST handwritten digit benchmark can be replicated with shallow non-convolutional neural networks. This is achieved by training such networks using the ‘Extreme Learning Machine’ (ELM) approach, which also enables a very rapid training time (∼ 10 minutes). Adding distortions, as is common practise for MNIST, reduces error rates even further. Our methods are also shown to be capable of achieving less than 5.5% error rates on the NORB image database. To achieve these results, we introduce several enhancements to the standard ELM algorithm, which individually and in combination can significantly improve performance. The main innovation is to ensure each hidden-unit operates only on a randomly sized and positioned patch of each image. This form of random ‘receptive field’ sampling of the input ensures the input weight matrix is sparse, with about 90% of weights equal to zero. Furthermore, combining our methods with a small number of iterations of a single-batch backpropagation method can significantly reduce the number of hidden-units required to achieve a particular performance. Our close to state-of-the-art results for MNIST and NORB suggest that the ease of use and accuracy of the ELM algorithm for designing a single-hidden-layer neural network classifier should cause it to be given greater consideration either as a standalone method for simpler problems, or as the final classification stage in deep neural networks applied to more difficult problems.

Connected through hyperedge HE-1 (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"
    - "motchallenge 2015 towards a benchmark for multi target tracking"
    - "enhanced image classification with a fast learning shallow convolutional neural network"
    - "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-2 (induced by source paper "an exploration of parameter redundancy in deep networks with circulant projections"):
  with members:
    - "enhanced image classification with a fast learning shallow convolutional neural network"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "enhanced image classification with a fast learning shallow convolutional neural network"):
  with members:
    - "deep learning in neural networks"
    - "explicit computation of input weights in extreme learning machines"
    - "constrained extreme learning machines a study on classification cases"
    - "convolutional kernel networks"
    - "online and adaptive pseudoinverse solutions for elm weights"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "2013 special issue learning the pseudoinverse solution to network weights"
    - "convolutional neural networks applied to house numbers digit classification"

Connected through hyperedge HE-4 (induced by source paper "hardware architecture for large parallel array of random feature extractors applied to image recognition"):
  with members:
    - "vlsi extreme learning machine a design space exploration"
    - "learning spike time codes through morphological learning with binary synapses"
    - "a neuromorphic hardware framework based on population coding"
    - "going deeper with convolutions"
    - "liquid state machine with dendritically enhanced readout for low power neuromorphic vlsi implementations"
    - "hardware amenable structural learning for spike based pattern classification using a simple model of active dendrites"
    - "network in network"

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)